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Comparison of machine learning techniques for spam detection

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Abstract

Email is a useful communication medium for better reach. There are two types of emails, those are ham or legitimate email and spam email. Spam is a kind of bulk or unsolicited email that contains an advertisement, phishing website link, malware, Trojan, etc. This research aims to classify spam emails using machine learning classifiers and evaluate the performance of classifiers. In the pre-processing step, the dataset has been analyzed in terms of attributes and instances. In the next step, thirteen machine learning classifiers are implemented for performing classification. Those classifiers are Adaptive Booster, Artificial Neural Network, Bootstrap Aggregating, Decision Table, Decision Tree, J48, K-Nearest Neighbor, Linear Regression, Logistic Regression, Naïve Bayes, Random Forest, Sequential Minimal Optimization and, Support Vector Machine. In terms of accuracy, the Random Forest classifier performs best and the performance of the Naïve Bayes classifier is substandard compared to the rest of the classifiers. Random Forest classifier had the accuracy of 99.91% and 99.93% for the Spam Corpus and Spambase datasets respectively. The naïve Bayes classifier had the accuracy of 87.63% and 79.53% for the Spam Corpus and Spambase datasets respectively.

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References

  1. Abdulhamid S’i M, Shuaib M, OluwafemiOsho II, Alhassan JK (2018) Comparative Analysis of Classification Algorithms for Email Spam Detection. Int J Comput Netw Inf Secur (IJCNIS) 10(1):60–67. https://doi.org/10.5815/ijcnis.2018.01.07

    Article  Google Scholar 

  2. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) “State-of-the-art in artificial neural network applications: A survey”, Heliyon, Volume 4, Issue 11, ISSN 2405–8440, https://doi.org/10.1016/j.heliyon.2018.e00938.

  3. Ali ABM S, Xiang Y (2007) “Spam Classification Using Adaptive Boosting Algorithm”, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)

  4. Alzahrani A, Rawat DB (2019) "Comparative Study of Machine Learning Algorithms for SMS Spam Detection," 2019 SoutheastCon, pp. 1–6, https://doi.org/10.1109/SoutheastCon42311.2019.9020530.

  5. Aminikhanghahi S, Shin S, Wang W, Son SH, Jeon SI (2014) An optimized support vector machine classifier to extract abnormal features from breast microwave tomography data. In Proceedings of the 2014 Conference on research in adaptive and convergent systems (RACS '14). Association for Computing Machinery, New York, NY, pp 111–115. https://doi.org/10.1145/2663761.2664230

    Book  Google Scholar 

  6. Anamika, KVL Padmini P, Guduru V, Sangeeta K (2015) Effect of Spam Filter on SPOT Algorithm. In Proceedings of the Third International Symposium on Women in Computing and Informatics (WCI '15). Association for Computing Machinery, New York, NY, USA, 640–643. https://doi.org/10.1145/2791405.2791552

  7. Bassiouni M, Ali M, El-Dahshan EA (2018) Ham and spam E-mails classification using machine learning techniques. J Appl Secur Res 13(3):315–331. https://doi.org/10.1080/19361610.2018.1463136

    Article  Google Scholar 

  8. Becker BG (1998) "Visualizing decision table classifiers," Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258), pp. 102–105, https://doi.org/10.1109/INFVIS.1998.729565.

  9. Bedmar IS, Samy D, Martinez JL. (2007) UC3M: classification of semantic relations between nominals using sequential minimal optimization. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval '07). Association for Computational Linguistics, USA, 382–385

  10. Bertsimas D, Li ML (2020) “Scalable holistic linear regression”, Oper Res Lett, https://doi.org/10.1016/j.orl.2020.02.008.

  11. David Bienvenido-Huertas, Carlos Rubio-Bellido, Juan Luis Pérez-Ordóñez, Miguel José Oliveira (2020) “Automation and optimization of in-situ assessment of wall thermal transmittance using a random Forest algorithm”, Build Environ 168, ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2019.106479

  12. Bin AbdRazak S, Bin Mohamad AF (2013) "Identification of spam email based on information from email header," 2013 13th International Conference on Intellient Systems Design and Applications, Bangi, pp. 347–353

  13. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In proceedings of the fifth annual workshop on computational learning theory (COLT '92). Association for Computing Machinery, New York, NY, USA, pp 144–152. https://doi.org/10.1145/130385.130401

    Book  Google Scholar 

  14. Braun AC, Weidner U, Hinz S (2011) "Support vector machines, import vector machines and relevance vector machines for hyperspectral classification — A comparison," 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4, https://doi.org/10.1109/WHISPERS.2011.6080861.

  15. Breiman, L (1996) Bagging Predict Mach Learn 24, 123–140. https://doi.org/10.1023/A:1018054314350

  16. Breiman L (2001) Random For Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  17. Bucurica M, Dogaru R, Dogaru I (2015) "A comparison of Extreme Learning Machine and Support Vector Machine classifiers," 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 471–474, https://doi.org/10.1109/ICCP.2015.7312705.

  18. Chen M, Challita U, Saad W, Yin C, Debbah M (2019) Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial. IEEE Commun Surv Tutorials 21(4):3039–3071. https://doi.org/10.1109/COMST.2019.2926625

    Article  Google Scholar 

  19. Chharia A, Gupta RK (2013) "Email classifier: An ensemble using probability and rules," 2013 Sixth International Conference on Contemporary Computing (IC3), pp. 130–136, https://doi.org/10.1109/IC3.2013.6612176.

  20. Cho J, Kim S (2020) “Personal and social predictors of use and non-use of fitness/diet app: application of random Forest algorithm”, Telematics Inf 55, ISSN 0736-5853, https://doi.org/10.1016/j.tele.2019.101301

  21. Cristianni N, Shawe-Talyor J (2000) “An introduction to support vector machines”, Cambridge Uninversity Press

  22. Cui J, Wang Y (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44(1):281–289, ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2010.10.004

    Article  Google Scholar 

  23. Diale M, Van Der Walt C, Celik T, Modupe A (2016) "Feature selection and support vector machine hyper-parameter optimisation for spam detection," 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), pp. 1–7, https://doi.org/10.1109/RoboMech.2016.7813162.

  24. Digamberrao KS, Prasad RS (2018) Author Identification using Sequential Minimal Optimization with rule-based Decision Tree on Indian Literature in Marathi. Proced Comput Sci 132:1086–1101, ISSN 1877–0509. https://doi.org/10.1016/j.procs.2018.05.024

    Article  Google Scholar 

  25. Dong Y, Ma X, Fu T (2020) Electrical load forecasting: A deep learning approach based on K-nearest neighbors, Appl Soft Comput J, https://doi.org/10.1016/j.asoc.2020.106900.

  26. Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. Trans Syst Man Cybern SMC-6(4):325–327. https://doi.org/10.1109/TSMC.1976.5408784

    Article  Google Scholar 

  27. Edla DR, Mangalorekar K, Dhavalikar G, Dodia S (2018) Classification of EEG data for human mental state analysis using Random Forest Classifier. Procedia Comput Sci 132:1523–1532, ISSN 1877–0509. https://doi.org/10.1016/j.procs.2018.05.116

    Article  Google Scholar 

  28. Ernawati S, Yulia ER, Frieyadie, Samudi (2018) "Implementation of The Naïve Bayes Algorithm with Feature Selection using Genetic Algorithm for Sentiment Review Analysis of Fashion Online Companies," 2018 6th International Conference on Cyber and IT Service Management (CITSM), pp. 1–5, https://doi.org/10.1109/CITSM.2018.8674286.

  29. Fujiwara Y, Ida Y, Kanai S, Kumagai A, Arai J, Ueda N (2019) Fast random Forest algorithm via incremental upper bound. In proceedings of the 28th ACM international conference on information and knowledge management (CIKM '19). Association for Computing Machinery, New York, NY, USA, pp 2205–2208. https://doi.org/10.1145/3357384.3358092

    Book  Google Scholar 

  30. Garner SR (n.d.) “WEKA: The Waikato Environment for Knowledge Analysis”, Available: https://www.cs.waikato.ac.nz/~ml/publications/1995/Garner95-WEKA.pdf

  31. Gavankar SS, Sawarkar SD (2017) "Eager decision tree," 2017 2nd International Conference for Convergence in Technology (I2CT), pp. 837–840, https://doi.org/10.1109/I2CT.2017.8226246.

  32. Gbenga DE, Christopher N, Yetunde DC (2017) Performance Comparison of Machine Learning Techniques for Breast Cancer Detection. Nova J Eng Appl Sci 6(1):1–8. https://doi.org/10.20286/nova-jeas-060105

    Article  Google Scholar 

  33. Gomes SR et al. (2017) "A comparative approach to email classification using Naive Bayes classifier and hidden Markov model," 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, pp. 482–487

  34. Gong C, Zhi-gang S, Wang P-h, Wang Q, Yang Y (2021) Evidential instance selection for K-nearest neighbor classification of big data. Int. J. Approx. Reason. 138:123–144, ISSN 0888-613X. https://doi.org/10.1016/j.ijar.2021.08.006

    Article  MathSciNet  MATH  Google Scholar 

  35. Guo Y., Bai L., Lao S., Wu S., Lew M.S. (2014) A Comparison between Artificial Neural Network and Cascade-Correlation Neural Network in Concept Classification. In: Ooi W.T., Snoek C.G.M., Tan H.K., Ho CK., Huet B., Ngo CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture notes in computer science, vol 8879. Springer, Cham https://doi.org/10.1007/978-3-319-13168-9_26

  36. Gupta P, Dubey RK, Mishra S (2019) “Detecting Spam Emails/Sms Using Naive Bayes and Support Vector Machine”, Int J Sci Technol Res, Volume 8, Issue 11

  37. Hassan MA, Mtetwa N (2018) "Feature Extraction and Classification of Spam Emails," 2018 5th international conference on Soft Computing & Machine Intelligence (ISCMI), Nairobi, Kenya, pp. 93–98

  38. He L, Yang X, Lu H (2007) "A Comparison of Support Vector Machines Ensemble for Classification," 2007 International Conference on Machine Learning and Cybernetics, pp. 3613–3617, https://doi.org/10.1109/ICMLC.2007.4370773.

  39. Heredia B, Khoshgoftaar TM, Prusa J, Crawford M (2016) "An Investigation of Ensemble Techniques for Detection of Spam Reviews," 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 127–133, https://doi.org/10.1109/ICMLA.2016.0029.

  40. Huang X, Shi L, Suykens JAK (2015) Sequential minimal optimization for SVM with pinball loss. Neurocomputing 149(Part C):1596–1603, ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2014.08.033

    Article  Google Scholar 

  41. Jain V, Phophalia A (2019) "Exponential Weighted Random Forest for Hyperspectral Image Classification," IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 3297–3300, https://doi.org/10.1109/IGARSS.2019.8897862.

  42. Jiang L, Cai Z, Wang D, Jiang S (2007) "Survey of Improving K-Nearest-Neighbor for Classification," Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), pp. 679–683, https://doi.org/10.1109/FSKD.2007.552.

  43. Joshi AV (2020) Decision trees. In: Machine Learning and Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-26622-6_6

    Book  MATH  Google Scholar 

  44. Kadlček F, Fučík O (2013) “Fast and energy efficient AdaBoost classifier”, in proceedings of the 10th FPGAworld conference (FPGAworld '13). Assoc Comput Mach New York NY USA 2:1–5. https://doi.org/10.1145/2513683.2513685

    Article  Google Scholar 

  45. Kalbhor M, Shrivastava S, Ujjainiya B (2013) "An artificial immune system with local feature selection classifier for spam filtering," in 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, India pp. 1–7.https://doi.org/10.1109/ICCCNT.2013.6726691

  46. Kalmegh SR (2018) Comparative analysis of the WEKA classifiers rules Conjunctiverule&Decisiontable on Indian news dataset by using different test mode. Int J Eng Sci Invent (IJESI) 7(2):01–09

    Google Scholar 

  47. Kang K, Gao F, Feng J (2018) "A New Multi-Layer Classification Method Based on Logistic Regression," 2018 13th International Conference on Computer Science & Education (ICCSE), pp. 1–4, https://doi.org/10.1109/ICCSE.2018.8468725

  48. Kaur J, Baghla S (2017) Modified decision table classifier by using decision support and confidence in online shopping dataset. Int J Comput Eng Technol 8(6):83–88

    Google Scholar 

  49. Kohavi R (1995) The power of decision tables. In proceedings of the 8th European conference on machine learning (ECML’95). Springer-Verlag, Berlin, Heidelberg, pp 174–189. https://doi.org/10.1007/3-540-59286-5_57

    Book  Google Scholar 

  50. Kohavi R, Sommerfield D (1998) Targeting business users with decision table classifiers. In proceedings of the fourth international conference on knowledge discovery and data mining (KDD'98). AAAI press, 249–253

  51. Kontsewaya Y, Antonov E, Artamonov A (2021) Evaluating the effectiveness of machine learning methods for spam detection. Proced Comput Sci 190:479–486, ISSN 1877-0509. https://doi.org/10.1016/j.procs.2021.06.056

    Article  Google Scholar 

  52. Kramer O (2013) K-nearest neighbors. In: dimensionality reduction with unsupervised nearest neighbors. Intelligent systems reference library, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38652-7_2

    Book  Google Scholar 

  53. Lei H, Long Q (2011) "Locate potential Support Vectors for faster Sequential Minimal Optimization," 2011 Seventh International Conference on Natural Computation, pp. 367–372, https://doi.org/10.1109/ICNC.2011.6022107.

  54. Li J, Huang S, He R, Qian K (2008) "Image Classification Based on Fuzzy Support Vector Machine," 2008 International Symposium on Computational Intelligence and Design, pp. 68–71, https://doi.org/10.1109/ISCID.2008.51.

  55. Lin C-F, Wang S-D (2002) Fuzzy support vector machines. Trans Neural Netw 13(2):464–471. https://doi.org/10.1109/72.991432

    Article  Google Scholar 

  56. Lin Z, Qiu D, Ergu D, Ying C, Liu K (2019) “A study on predicting loan default based on the random forest algorithm”, Proced Comput Sci, Volume 162, Pages 503–513, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.12.017.

  57. Lin L, Dekkers IA, Tao Q, Lamb HJ (n.d.) “Novel artificial neural network and linear regression based equation for estimating visceral adipose tissue volume, Clin Nutr”, https://doi.org/10.1016/j.clnu.2020.02.013.

  58. Liu Y-Z, Yao H-X, Gao W, Zhao D-B (2005) Single sequential minimal optimization: an improved SVMs training algorithm. 2005 Int Conf Mach Learn Cybern 7:4360–4364. https://doi.org/10.1109/ICMLC.2005.1527705

    Article  Google Scholar 

  59. Lv C, Chen D-R (2018) “Interpretable Functional Logistic Regression”, CSAE ‘18, October 22–24, Hohhot, China, https://doi.org/10.1145/3207677.3277962

  60. Ma CJ, Ding ZS (2020) "Improvement of k-nearest neighbor algorithm based on double filtering," 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), pp. 1567–1570, https://doi.org/10.1109/ICMCCE51767.2020.00343.

  61. Matharasi B, Senthilrajan A (2017) Sentiment Analysis of Twitter Data using Naive bayes with Unigran Approach. Int J Sci Res Publ 7(Issue – 5) ISSN: 2250-3153:337–341

    Google Scholar 

  62. Mendez JR, Cotos-Yanez TR, Ruano-Ordas D (2019) A new semantic-based feature selection method for spam filtering. Appl Soft Comput J 76:89–104

    Article  Google Scholar 

  63. Moon S-H, Kim Y-H (2019) “An improved forecast of precipitation type using correlation-based feature selection and multinomial logistic regression”, Atmos Res, https://doi.org/10.1016/j.atmosres.2020.104928

  64. More AS, Rana DP (2017) "Review of random forest classification techniques to resolve data imbalance," 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), pp. 72–78, https://doi.org/10.1109/ICISIM.2017.8122151.

  65. Nasreen M Shajideen, BV (2018) “Spam filtering: a comparison between different machine learning classifiers”, proceedings of the 2nd international conference on electronics, communication and aerospace technology (ICECA 2018)

  66. Noronha DH, Torquato MF, Fernandes MAC (2019) A parallel implementation of sequential minimal optimization on FPGA. Microprocess Microsyst 69:138–151, ISSN 0141-9331. https://doi.org/10.1016/j.micpro.2019.06.007

    Article  Google Scholar 

  67. Okfalisa, IG, Mustakim, Reza NGI (2017) "Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 294–298, https://doi.org/10.1109/ICITISEE.2017.8285514.

  68. Osegi EN, Jumbo EF (2021) “Comparative analysis of credit card fraud detection in simulated annealing trained artificial neural network and hierarchical temporal memory”, Mach Learn Appl 6, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2021.100080

  69. Paing MP, Pintavirooj C, Tungjitkusolmun S, Choomchuay S, Hamamoto K (2018) "Comparison of Sampling Methods for Imbalanced Data Classification in Random Forest," 2018 11th Biomedical Engineering International Conference (BMEiCON), pp. 1–5, https://doi.org/10.1109/BMEiCON.2018.8609946.

  70. Panhalkar AR, Doye DD (2021) “Optimization of decision trees using modified African buffalo algorithm”, J King Saud Univ Comput Inf Sci, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2021.01.011.

  71. Panigrahi PK (2012) "A Comparative Study of Supervised Machine Learning Techniques for Spam E-mail Filtering," 2012 Fourth International Conference on Computational Intelligence and Communication Networks, pp. 506–512, https://doi.org/10.1109/CICN.2012.14.

  72. Panigrahi R, Borah S (2018) Rank Allocation to J48 Group of Decision Tree Classifiers using Binary and Multiclass Intrusion Detection Datasets. Int Conf Comput Intell Data Sci (ICCIDS 2018), Procedia Comput Therm Sci 132:323–332

    Google Scholar 

  73. Patel DR, Kiran MB (n.d.) “A non-contact approach for surface roughness prediction in CNC turning using a linear regression model”, Mater Today Proceed, https://doi.org/10.1016/j.matpr.2019.12.029

  74. Patel R, Thakkar P (2014) "Opinion Spam Detection Using Feature Selection," 2014 International Conference on Computational Intelligence and Communication Networks, pp. 560–564, https://doi.org/10.1109/CICN.2014.127.

  75. Patil S, Kulkarni U (2019) "Accuracy Prediction for Distributed Decision Tree using Machine Learning approach," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1365–1371, https://doi.org/10.1109/ICOEI.2019.8862580.

  76. Paul A, Mukherjee DP (2016) Reinforced random forest. In proceedings of the tenth Indian conference on computer vision, graphics and image processing (ICVGIP '16). Assoc Comput Mach New York NY USA 1:1–8. https://doi.org/10.1145/3009977.3010003

    Article  Google Scholar 

  77. Pelle R, Alcântara C, Moreira VP (2018) “A Classifier Ensemble for Offensive Text Detection”, WebMedia ‘18, October 16–19, Salvador-BA, Brazil, https://doi.org/10.1145/3243082.3243111

  78. Peng X, Xu D (2013) A twin-hypersphere support vector machine classifier and the fast learning algorithm. Inf. Sci. 221:12–27, ISSN 0020-0255. https://doi.org/10.1016/j.ins.2012.09.009

    Article  MathSciNet  MATH  Google Scholar 

  79. Pisner DA, Schnyer DM (2020) “Chapter 6 - Support vector machine”, Editor(s): Andrea Mechelli, Sandra Vieira, Machine Learning, Academic Press, Pages 101–121, ISBN 9780128157398, https://doi.org/10.1016/B978-0-12-815739-8.00006-7.

  80. Platt, J (1998) Sequential minimal optimization : a fast algorithm for training support vector machines. Microsoft Res Tech Rep

  81. Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods: support vector learning. MIT press, Cambridge, MA, USA, pp 185–208

    Google Scholar 

  82. Platt JC (1999) Using analytic QP and sparseness to speed training of support vector machines. In proceedings of the 1998 conference on advances in neural information processing systems II. MIT press, Cambridge, MA, USA, pp 557–563

    Google Scholar 

  83. Provost, J (1999) “Na ive-Bayes vs. Rule-Learning in Classification of Email.”Available: http://www.cs.utexas.edu/ftp/AI-Lab/tech-reports/UT-AI-TR-99-284.pdf

  84. Quinlan JR (1996) Learning decision tree classifiers. ACM Comput Surv 28(1):71–72. https://doi.org/10.1145/234313.234346

    Article  Google Scholar 

  85. Rachida I, Abdelwahed N, Sanaa E F (2019) “J48 Algorithms of machine learning for predicting user’s the acceptance of an E-orientation Systems”, SCA2019, October 2–4, Casablanca, Morocco

  86. Rathod SB, Pattewar TM (2015) "Content based spam detection in email using Bayesian classifier," 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, pp. 1257–1261, https://doi.org/10.1109/ICCSP.2015.7322709.

  87. Sahingoz OK, Buber E, Onder Demir BD (2019) Machine learning based phishing detection from URLs. Exp Syst Appl 117:345–357, ISSN 0957–4174. https://doi.org/10.1016/j.eswa.2018.09.029

    Article  Google Scholar 

  88. Sahoo SR, Gupta BB (2020) Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection. Enterp Inf Syst 14(5):710–736. https://doi.org/10.1080/17517575.2020.1712742

    Article  Google Scholar 

  89. Saidani N, Adi K, Allili MS (2020) “A semantic-based classification approach for an enhanced spam detection”, Comput Secur 94, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101716

  90. Schapire RE (2013) Explaining AdaBoost. In: Schölkopf B, Luo Z, Vovk V (eds) Empirical Inference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5

    Chapter  Google Scholar 

  91. ShafighAski A, Sourati NK (2016) Proposed efficient algorithm to filter spam using machine learning techniques. Pac Sci Rev A Nat Sci Eng 18(2):145–149

    Google Scholar 

  92. Shah N, Jain S (2019) "Detection of Disease in Cotton Leaf using Artificial Neural Network," 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 473–476, https://doi.org/10.1109/AICAI.2019.8701311.

  93. Shahbudin S, Hussain A, Samad SA, Md Tahir N (2008) "Training and analysis of Support Vector Machine using Sequential Minimal Optimization," 2008 IEEE International Conference on Systems, Man and Cybernetics, pp. 373–378, https://doi.org/10.1109/ICSMC.2008.4811304.

  94. Sharma A, Suryawanshi A (2016) “A Novel Method for Detecting Spam Email using KNN Classification with Spearman Correlation as Distance Measure”, Int J Comput Appl (0975–8887) Volume 136, No.6, https://doi.org/10.5120/ijca2016908471

  95. Sharma AK, Yadav R (2015) “Spam Mails Filtering Using Different Classifiers with Feature Selection and Reduction Techniques”, 2015 Fifth International Conference on Communication Systems and Network Technologies

  96. Shubhangi DC, Hiremath PS (2009) Support vector machine (SVM) classifier for brain tumor detection. In proceedings of the international conference on advances in computing, communication and control (ICAC3 '09). Association for Computing Machinery, New York, NY, USA, pp 444–448. https://doi.org/10.1145/1523103.1523191

    Book  Google Scholar 

  97. Singh AK, Bhushan S, Vij S(2019) “filtering spam messages and mails using fuzzy C means algorithm”, 2019 4th international conference on internet of things: smart innovation and usages (IoT-SIU), Ghaziabad, India, pp. 1–5

  98. Subasi A, Alzahrani S, Aljuhani A, Aljedani M (2018) "Comparison of Decision Tree Algorithms for Spam E-mail Filtering," 2018 1st international conference on computer applications & information security (ICCAIS), Riyadh, pp. 1–5

  99. Sun S, Huang R (2010) "An adaptive k-nearest neighbor algorithm," 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 91–94, https://doi.org/10.1109/FSKD.2010.5569740.

  100. Suriya Prakash J, Annamalai Vignesh K, Ashok C, Adithyan R (2012) "Multi class Support Vector Machines classifier for machine vision application," 2012 International Conference on Machine Vision and Image Processing (MVIP), pp. 197–199, https://doi.org/10.1109/MVIP.2012.6428794.

  101. Susai Mary J, Sai Balaji MA, Krishnakumari A, Nakandhrakumar RS, Dinakaran D (2019) Monitoring of drill runout using Least Square support vector machine classifier. Measurement 146:24–34, ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2019.05.102

    Article  Google Scholar 

  102. Suthaharan S (2016) Support vector machine. In: machine learning models and algorithms for big data classification. Integrated series in information systems, vol 36. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7641-3_9

    Book  MATH  Google Scholar 

  103. Tina R Patil, SSS (2013) “Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification”, Int J Comput Sci Appl Vol. 6, No.2 ISSN: 0974–1011

  104. Tran T, Tsai P, Jan T (2008) "An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering," in ICPR 2008 19th international conference on pattern recognition, Tampa, FLhttps://doi.org/10.1109/ICPR.2008.4761358

  105. Tretyakov K (n.d.) “Machine Learning Techniques in Spam Filtering”, Available: https://courses.cs.ut.ee/2004/dm-seminar-spring/uploads/Main/P06.pdf

  106. Tseng C, Chen M (2009) "Incremental SVM Model for Spam Detection on Dynamic Email Social Networks," 2009 International Conference on Computational Science and Engineering, pp. 128–135, https://doi.org/10.1109/CSE.2009.260.

  107. Turčaník M (2015) "Packet filtering by artificial neural network," Int Conf Mil Technol (ICMT), 2015, pp. 1–4, https://doi.org/10.1109/MILTECHS.2015.7153739.

  108. Urmaliya A, Singhai J (2013) "Sequential minimal optimization for support vector machine with feature selection in breast cancer diagnosis," 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pp. 481–486, https://doi.org/10.1109/ICIIP.2013.6707638.

  109. Vanhoenshoven F, Nápoles G, Falcon R, Vanhoof K, Köppen M (2016) "Detecting malicious URLs using machine learning techniques," 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, pp. 1–8, https://doi.org/10.1109/SSCI.2016.7850079.

  110. Vapnik VN (1998) Statistical learning theory. John Wiley & Sons

  111. Vapnik VN (1999) An overview of statistical learning theory. Trans Neural Netw 10(5):988–999. https://doi.org/10.1109/72.788640

    Article  Google Scholar 

  112. Vapnik VN (2000) Methods of pattern recognition. In: The Nature of Statistical Learning Theory. Statistics for Engineering and Information Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3264-1_6

    Book  Google Scholar 

  113. Vijayanand R, Devaraj D, Kannapiran B (2018) Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Comput Secur 77:304–314, ISSN 0167-4048. https://doi.org/10.1016/j.cose.2018.04.010

    Article  Google Scholar 

  114. Wang SC (2003) Artificial neural network. In: interdisciplinary computing in Java programming. The springer international series in engineering and computer science, vol 743. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0377-4_5

    Book  Google Scholar 

  115. Wang L-S, Xu Y-T, Zhao L-S (2005) "A kind of hybrid classification algorithm based on rough set and support vector machine," 2005 International Conference on Machine Learning and Cybernetics, pp. 1676-1679 Vol. 3, https://doi.org/10.1109/ICMLC.2005.1527214.

  116. Wang S, Aggarwal C, Liu H (2018) Random-Forest-Inspired Neural Networks. ACM Trans Intell Syst Technol 9(6):Article 69. https://doi.org/10.1145/3232230

    Article  Google Scholar 

  117. Wang F, Wang Q, Nie F, Li Z, Yu W, Ren F (2020, ISSN 0031-3203) A linear multivariate binary decision tree classifier based on K-means splitting. Pattern Recognit 107:107521. https://doi.org/10.1016/j.patcog.2020.107521

    Article  Google Scholar 

  118. Wei R, Ghosal S (2020) Contraction properties of shrinkage priors in logistic regression. J Stat Plann Infer 207:215–229. https://doi.org/10.1016/j.jspi.2019.12.004

    Article  MathSciNet  MATH  Google Scholar 

  119. Witt G (2012) Chapter 3 - A brief history of rules, Editor(s): Graham Witt, Writing Effective Business Rules, Morgan Kaufmann, Pages 25–63, ISBN 9780123850515, https://doi.org/10.1016/B978-0-12-385051-5.00003-3.

  120. Wu S, Tong X, Wang W, Xin G, Wang B, Zhou Q (2018) Website defacements detection based on support vector machine classification method. In proceedings of the 2018 international conference on computing and data engineering (ICCDE 2018). Association for Computing Machinery, New York, NY, USA, pp 62–66. https://doi.org/10.1145/3219788.3219804

    Book  Google Scholar 

  121. Yang F (2019) "An Extended Idea about Decision Trees," 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 349–354, https://doi.org/10.1109/CSCI49370.2019.00068.

  122. Yasin W, Ibrahim H Intelligent Cooperative Least Recently Used Web Caching Policy based on J48 Classifier. iiWAS2014 Hanoi, Vietnam

  123. Yuan P, Ren S, Xu H, Chen J (2018) "Chrysanthemum Abnormal Petal Type Classification using Random Forest and Over-sampling," 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 275–278, https://doi.org/10.1109/BIBM.2018.8621234.

  124. Zhang Z (2018) Artificial neural network. In: Multivariate Time Series Analysis in Climate and Environmental Research. Springer, Cham. https://doi.org/10.1007/978-3-319-67340-0_1

    Book  Google Scholar 

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Funding

This research work has been written with the financial support of Rashtriya Uchchatar Shiksha Abhiyan (RUSA- Phase 2.0) grant sanctioned vide Letter No. F.24–51/2014-U, Policy (TNMulti-Gen), Dept. of Edn. Govt. of India, Dt. 09.10.2018.

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    Argha Ghosh & A. Senthilrajan

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Ghosh, A., Senthilrajan, A. Comparison of machine learning techniques for spam detection. Multimed Tools Appl 82, 29227–29254 (2023). https://doi.org/10.1007/s11042-023-14689-3

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