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 shapebased leaf image feature extraction, this paper discusses the evaluation of several methods available in Weka and a wrapperbased genetic algorithm feature selection

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 shapebased leaf image feature extraction, this paper discusses the evaluation of several methods available in Weka and a wrapperbased genetic algorithm feature selection

Feature selection for Malaysian medicinal plant leaf shape identification and classification

Malaysian medicinal plants may be abundant natural resources but there has not been much research done on preserving the knowledge of these medicinal plants which enables general public to know the leaf using computing capability.Therefore, in this preliminary study, a novel framework in order to identify and classify tropical medicinal plants in Malaysia based on the extracted patterns from the leaf is presented.The extracted patterns from medicinal plant leaf are obtained based on several angle features.However, the extracted features create quite large number of attributes (features), thus degrade the performance most of the classifiers.Thus, a feature selection is applied to leaf data and to investigate whether the performance of a classifier can be improved.Wrapper based genetic algorithm (GA) feature selection is used to select the features and the ensemble classifier called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) is used as a classifier.The performance of the feature selection is compared with two feature selections from Weka.In the experiment, five species of Malaysian medicinal plants are identified and classified in which will be represented by using 65 images.This study is important in order to assist local community to utilize the knowledge and application of Malaysian medicinal plants for future generation