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Large-Scale Hierarchical Classification with Feature Selection

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Large Scale Hierarchical Classification: State of the Art

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

LSHC involves dataset consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale problem. It speeds up the training process, reduces the prediction time, and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improving the classification accuracy by removing irrelevant features. In this chapter, we investigate various filter-based feature selection methods for dimensionality reduction to solve the LSHC problem.

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References

  1. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1(3), 131–156 (1997)

    Article  Google Scholar 

  2. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology 3(02), 185–205 (2005)

    Article  Google Scholar 

  3. Gopal, S., Yang, Y.: Distributed training of large-scale logistic models. In: Proceedings of the 30th International Conference on Machine Learning (ICML), pp. 289–297 (2013)

    Google Scholar 

  4. Gopal, S., Yang, Y.: Recursive regularization for large-scale classification with hierarchical and graphical dependencies. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 257–265 (2013)

    Google Scholar 

  5. Heisele, B., Serre, T., Prentice, S., Poggio, T.: Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition 36(9), 2007–2017 (2003)

    Article  Google Scholar 

  6. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: European Conference on Machine Learning, pp. 137–142 (1998)

    Google Scholar 

  7. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial intelligence 97(1), 273–324 (1997)

    Article  Google Scholar 

  8. Naik, A., Rangwala, H.: A ranking-based approach for hierarchical classification. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10 (2015)

    Google Scholar 

  9. Ogura, H., Amano, H., Kondo, M.: Feature selection with a measure of deviations from poisson in text categorization. Expert Systems with Applications 36(3), 6826–6832 (2009)

    Article  Google Scholar 

  10. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  11. Ristoski, P., Paulheim, H.: Feature selection in hierarchical feature spaces. In: International Conference on Discovery Science, pp. 288–300. Springer (2014)

    Google Scholar 

  12. Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y., Wang, Z.: A novel feature selection algorithm for text categorization. Expert Systems with Applications 33(1), 1–5 (2007)

    Article  Google Scholar 

  13. Strobl, C., Zeileis, A.: Danger: High power! exploring the statistical properties of a test for random forest variable importance. In: In Proceedings of the 18th International Conference on Computational Statistics (2008)

    Google Scholar 

  14. Tang, J., Alelyani, S., Liu, H.: Feature selection for classification: A review. Data Classification: Algorithms and Applications p. 37 (2014)

    Google Scholar 

  15. Wibowo, W., Williams, H.E.: Simple and accurate feature selection for hierarchical categorisation. In: Proceedings of the 2002 ACM symposium on Document Engineering, pp. 111–118 (2002)

    Google Scholar 

  16. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)

    Google Scholar 

  17. Zheng, Z., Wu, X., Srihari, R.: Feature selection for text categorization on imbalanced data. ACM Sigkdd Explorations Newsletter 6(1), 80–89 (2004)

    Article  Google Scholar 

  18. Zhou, D., Xiao, L., Wu, M.: Hierarchical classification via orthogonal transfer. In: Proceedings of the 28th International Conference on Machine Learning (ICML), pp. 801–808 (2011)

    Google Scholar 

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Naik, A., Rangwala, H. (2018). Large-Scale Hierarchical Classification with Feature Selection. In: Large Scale Hierarchical Classification: State of the Art. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-01620-3_4

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  • DOI: https://doi.org/10.1007/978-3-030-01620-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01619-7

  • Online ISBN: 978-3-030-01620-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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