Optimal Feature Subset Selection for Imbalanced Class Data using SMOTE and Binary ALO Algorithm

Abstract

Feature selection in multispectral high dimensional information is a hard labour machine learning problem because of the imbalanced classes present in the data. The existing Most of the feature selection schemes in the literature ignore the problem of class imbalance by choosing the features from the classes having more instances and avoiding significant features of the classes having less instances. In this paper, SMOTE concept is exploited to produce the required samples form minority classes. Feature selection model is formulated with the objective of reducing number of features with improved classification performance. This model is based on dimensionality reduction by opt for a subset of relevant spectral, textural and spatial features while eliminating the redundant features for the purpose of improved classification performance. Binary ALO is engaged to solve the feature selection model for optimal selection of features. The proposed ALO-SVM with wrapper concept is applied to each potential solution obtained during optimization step. The working of this methodology is tested on LANDSAT multispectral image.