An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features

Multi-class imbalance shape-based leaf image features requires feature subset that appropriately represent the leaf shape. multi-class imbalance data is a type of data classification problem in which some data classes is highly underrepresented compared to others. this occurs when at least one data class is represented by just a few numbers of training samples known as the minority class compared to other classes that make up the majority class. to address this issue in shape-based leaf image feature extraction,this paper discusses the evaluation of several methods available in weka and a wrapper-based genetic algorithm feature selection.