research-article

A Comparative Study for Classification on Different Domain

Authors:

Noviyanti Tri Maretta Sagala,

Jenq-Haur WangAuthors Info & Claims

ICMLC '18: Proceedings of the 2018 10th International Conference on Machine Learning and Computing

Pages 1 - 5

https://doi.org/10.1145/3195106.3195129

Published: 26 February 2018 Publication History

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Abstract

There is no individual classification technique has been shown to deal with all kinds of classification problems. The objective is to select the technique which more possibly reaches the best performance for any domain of data set. We focus on classifying datasets in different domains and properties such as numerical, categorical, and textual. We deal with one versus all strategy to handle multi-class problems. In the experiment, we compared the performance of 4 classification techniques namely Boosted C5.0, KNN, Naïve Bayes, and SVM on 10-fold cross-validation on different number of features. For numerical data set (low and high dimensional data set), the performance of KNN was better than other classification methods. For a categorical and textual data set, Naïve Bayes and SVM were outperformed, respectively.

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Saidi ABen Othman SDhouibi MBen Saoud S(2022)FPGA-based implementation of classification techniquesIntegration, the VLSI Journal10.1016/j.vlsi.2021.08.00481:C(280-299)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.vlsi.2021.08.004
Sagala NPermai S(2021)Predictive Model using SVM to Improve the Effectiveness of Direct Marketing2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS53897.2021.9574440(1-6)Online publication date: 8-Sep-2021
https://doi.org/10.1109/AiDAS53897.2021.9574440
Torres GXavier Guterres MRafael Rezende Celestino V(undefined)Legal Actions in Brazilian Air Transport: A Machine Learning and Multinomial Logistic Regression AnalysisSSRN Electronic Journal10.2139/ssrn.4185448
https://doi.org/10.2139/ssrn.4185448

Index Terms

A Comparative Study for Classification on Different Domain
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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Published In

ICMLC '18: Proceedings of the 2018 10th International Conference on Machine Learning and Computing

February 2018

411 pages

ISBN:9781450363532

DOI:10.1145/3195106

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2018

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Refereed limited

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Ministry of Science and Technology, Taiwan

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ICMLC 2018

ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing

February 26 - 28, 2018

Macau, China

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Saidi ABen Othman SDhouibi MBen Saoud S(2022)FPGA-based implementation of classification techniquesIntegration, the VLSI Journal10.1016/j.vlsi.2021.08.00481:C(280-299)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.vlsi.2021.08.004
Sagala NPermai S(2021)Predictive Model using SVM to Improve the Effectiveness of Direct Marketing2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS53897.2021.9574440(1-6)Online publication date: 8-Sep-2021
https://doi.org/10.1109/AiDAS53897.2021.9574440
Torres GXavier Guterres MRafael Rezende Celestino V(undefined)Legal Actions in Brazilian Air Transport: A Machine Learning and Multinomial Logistic Regression AnalysisSSRN Electronic Journal10.2139/ssrn.4185448
https://doi.org/10.2139/ssrn.4185448

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A novel Bagged Naïve Bayes-Decision Tree approach for multi-class classification problems

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