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Eager Hierarchical Feature Selection

Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

This chapter discusses four different eager hierarchical feature selection methods, i.e. Tree-based Feature Selection (TSEL) (Jeong and Myaeng, Proceedings of the international joint conference on natural language processing, Nagoya, Japan, 2013, [1]) , Bottom-up Hill Climbing Feature Selection (HC) (Wang et al, Proceedings of the 26th Australasian computer science conference, Darlinghurst, Australia, 2003, [5]) , Greedy Top-down Feature Selection (GTD) (Lu et al, Proceedings of the international conference conference on collaborative computing, Austin, USA, 2013, [2]) and Hierarchy-based Feature Selection (SHSEL) (Ristoski and Paulheim, Proceedings of the international conference on discovery science (DS 2014), 2014, [3]) . All of those four hierarchical feature selection methods are also categorised as filter methods. Those methods aim to alleviate the feature redundancy by considering the hierarchical structure between features and the predictive power of features (e.g. information gain). Unlike the lazy hierarchical feature selection methods discussed in last chapter, those eager hierarchical feature selection methods only consider the relevance value of those features calculated by the training dataset and the hierarchical information, without considering the actual value of features for individual testing instance.

References

  1. 1.
    Jeong Y, Myaeng S (2013) Feature selection using a semantic hierarchy for event recognition and type classification. In: Proceedings of the international joint conference on natural language processing, Nagoya, Japan, pp 136–144Google Scholar
  2. 2.
    Lu S, Ye Y, Tsui R, Su H, Rexit R, Wesaratchakit S, Liu X, Hwa R (2013) Domain ontology-based feature reduction for high dimensional drug data and its application to 30-day heart failure readmission prediction. In: Proceedings of the international conference conference on collaborative computing, Austin, USA, pp 478–484Google Scholar
  3. 3.
    Ristoski P, Paulheim H (2014) Feature selection in hierarchical feature spaces. In: Proceedings of the international conference on discovery science (DS 2014), pp 288–300Google Scholar
  4. 4.
    Wan C, Freitas AA (2015) Two methods for constructing a gene ontology-based feature selection network for a Bayesian network classifier and applications to datasets of aging-related genes. In: Proceedings of the sixth ACM conference on bioinformatics, computational biology and health informatics (ACM-BCB 2015), Atlanta, USA, pp 27–36Google Scholar
  5. 5.
    Wang BB, Mckay RIB, Abbass HA, Barlow M (2003) A comparative study for domain ontology guided feature extraction. In: Proceedings of the 26th Australasian computer science conference, Darlinghurst, Australia, pp 69–78Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

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