EFS-MI: an ensemble feature selection method for classification View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06

AUTHORS

Nazrul Hoque, Mihir Singh, Dhruba K. Bhattacharyya

ABSTRACT

Feature selection methods have been used in various applications of machine learning, bioinformatics, pattern recognition and network traffic analysis. In high dimensional datasets, due to redundant features and curse of dimensionality, a learning method takes significant amount of time and performance of the model decreases. To overcome these problems, we use feature selection technique to select a subset of relevant and non-redundant features. But, most feature selection methods are unstable in nature, i.e., for different training datasets, a feature selection method selects different subsets of features that yields different classification accuracy. In this paper, we provide an ensemble feature selection method using feature–class and feature-feature mutual information to select an optimal subset of features by combining multiple subsets of features. The method is validated using four classifiers viz., decision trees, random forests, KNN and SVM on fourteen UCI, five gene expression and two network datasets. More... »

PAGES

105-118

References to SciGraph publications

  • 2012. Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes in ADVANCES IN COMPUTER SCIENCE AND INFORMATION TECHNOLOGY. COMPUTER SCIENCE AND ENGINEERING
  • 2017-09. Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion in NEURAL COMPUTING AND APPLICATIONS
  • 2017-06. An insight into imbalanced Big Data classification: outcomes and challenges in COMPLEX & INTELLIGENT SYSTEMS
  • 1996-08. Bagging predictors in MACHINE LEARNING
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