List of Articles Instance selection

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  1 - Feature Selection and Cancer Classification Based on Microarray Data Using Multi-Objective Cuckoo Search Algorithm
  kh. Kamari f. rashidi a. Khalili
  Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Mo More

  Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective cuckoo search algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. The proposed algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy. Manuscript profile
• Open Access Article
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  2 - Combination of Instance Selection and Data Augmentation Techniques for Imbalanced Data Classification
  Parastoo Mohaghegh Samira Noferesti Mehri Rajaei
  Mohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method fo More

  Mohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method for decision making and prediction. Classification algorithms usually work well on balanced datasets. However, one of the challenges of the classification algorithms is how to correctly predicting the label of new samples based on learning on imbalanced datasets. In this type of dataset, the heterogeneous distribution of the data in different classes causes examples of the minority class to be ignored in the learning process, while this class is more important in some prediction problems. To deal with this issue, in this paper, an efficient method for balancing the imbalanced dataset is presented, which improves the accuracy of the machine learning algorithms to correct prediction of the class label of new samples. According to the evaluations, the proposed method has a better performance compared to other methods based on two common criteria in evaluating the classification of imbalanced datasets, namely "Balanced Accuracy" and "Specificity". Manuscript profile