Advertisement

Lazy Hierarchical Feature Selection

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

Abstract

This chapter describes three different lazy hierarchical feature selection methods, namely Select Hierarchical Information-Preserving Features (HIP) (Wan and Freitas, Artificial intelligence review, [5], Wan et al., IEEE/ACM Trans Comput Biol Bioinform 12(2):262–275, [6]) , Select Most Relevant Features (MR) (Wan and Freitas, Artificial intelligence review, [5], Wan et al., IEEE/ACM Trans Comput Biol Bioinform 12(2):262–275, [6]) and the hybrid Select Hierarchical Information-Preserving and Most Relevant Features (HIP–MR) (Wan and Freitas, Proceedings of IEEE international conference on bioinformatics and biomedicine (BIBM 2013), Shanghai, China, pp 373–380, [3], Wan et al., IEEE/ACM Trans Comput Biol Bioinform 12(2):262–275, [6]) . Those three hierarchical feature selection methods are categorised as filter methods (discussed in Chap.  2, i.e. feature selection is conducted before the learning process of classifier).

References

  1. 1.
    Pereira RB, Plastino A, Zadrozny B, de C Merschmann LH, Freitas AA (2011) Lazy attribute selection: choosing attributes at classification time. Intell Data Anal 15(5):715–732CrossRefGoogle Scholar
  2. 2.
    Stanfill C, Waltz D (1986) Toward memory-based reasoning. Commun ACM 29(12):1213–1228CrossRefGoogle Scholar
  3. 3.
    Wan C, Freitas AA (2013) Prediction of the pro-longevity or anti-longevity effect of Caenorhabditis Elegans genes based on Bayesian classification methods. In: Proceedings of IEEE international conference on bioinformatics and biomedicine (BIBM 2013), Shanghai, China, pp 373–380Google 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.
    Wan C, Freitas AA (2017) An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features. In: Artificial intelligence reviewGoogle Scholar
  6. 6.
    Wan C, Freitas AA, de Magalhães JP (2015) Predicting the pro-longevity or anti-longevity effect of model organism genes with new hierarchical feature selection methods. IEEE/ACM Trans Comput Biol Bioinform 12(2):262–275Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

Personalised recommendations