A direct ensemble classifier for imbalanced multiclass learning

Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. A combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers.The DECIML framework has been tested with several benchmark dataset and shows promising results

Ensemble classifier and resampling for imbalanced multiclass learning

An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifie

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

Improving the identification and classification of Malaysian medicinal leaf images using ensemble method

Malaysia has abundant natural resources especially plants which can be used for medicinal or herbal purposes. However, there is less research to preserve the knowledge of these resources to be utilized by the community in identifying useful medicinal plants using computing tools. This paper presents the implementation of digital opportunities for Malaysian medicinal plants via leaf image identification and classification. Of late, experts in traditional medicine and herbs have become few and the younger generation are mostly unknowledgeable about the medicinal and herbal properties of the plants. Therefore, this work is important in assisting the community (rural and urban) to identify and possibly share the knowledge of Malaysian medicinal plants with the future generation. The focus of this paper is to prepare the identification phase before the actual system is developed. Thus, the implementation of such a system is vital in order to enable the community to preserve these important resources

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.This study proposes a framework to identify and classify tropical medicinal plants in Malaysia based the extracted patterns from the leaf.The extracted patterns from medicinal plant leaf are obtained based on several angle features.Five classifiers, obtained from WEKA and an ensemble classifier, called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML), are used to compare their performance accuracies over this data.In this experiment, five species of Malaysian medicinal plants are identified and classified in which each species will be represented by using 65 images.This study is important in order to assist local community to utilize the knowledge discovery and application of Malaysian medicinal plants for future generation