BINARY CUCKOO OPTIMIZATION ALGORITHM FOR FEATURE SELECTION IN HIGH-DIMENSIONAL DATASETS

Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

BPJ01_614

تاریخ نمایه سازی: 29 دی 1392

Abstract:

Feature selection is a process commonly used in machine learning. Based on Binary Cuckoo Optimization Algorithm (BCOA) and information theory, this paper proposes a new filter feature selection method for classification problems. The proposed algorithm is based on BCOA and the Mutual Information (MI) of each pair of features, which determines the relevance and redundancy of the selected feature subset. Different weights for the relevance and redundancy in the fitness functions of the proposed algorithm are used to further improve their performance in terms of the number of features and the classification accuracy. In the experiments, an Artificial Neural Network (ANN) is employed to evaluate the classification accuracy of the selected feature subset on the test sets of six datasets. The results show that proposed algorithms can significantly reduce the number of features and achieve high classification accuracy in almost all cases.

Authors

Behnoosh Molaei

Department of computer, Dezful Branch, Islamic Azad University, Dezful Iran

Seyedeh Parvaneh Hosseini

Sama technical and vocational training college, Islamic Azad University, Ahwaz Branch, Ahwaz, Iran

Alireza Assareh

Engineering Faculty, Computer Engineering Department, Chamran University of Ahwaz, Ahwaz, Iran

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