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Recent Articles

2020-01-30 Review Article

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

Abstract

Genetic datasets have a large number of features that may significantly affect the disease classification process, especially datasets related to cancer diseases. Evolutionary algorithms (EA) are used to find the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete. In this paper, a method of gene selection was proposed two sequent stages: in the first stage, the fuzzy mutual information (FMI) method is used to choose the most important genes selected through a fuzzy model that was built based on the dataset size. In the second stage, the BBA is used to reduce and determine a fixed number of genes affecting the process of classification, which came from the first stage. The proposed algorithm, FMI_BBA, describes efficiency, by obtaining a higher classification accuracy and a few numbers of selected genes compared to other algorithms.

Abstract Read Full Article HTML DOI: 10.29328/journal.apb.1001009 Cite this Article

2020-02-11 Research Article

Improving cancer diseases classification using a hybrid filter and wrapper feature subset selection

Abstract

In the classification of cancer data sets, we note that they contain a number of additional features that influence the classification accuracy. There are many evolutionary algorithms that are used to define the feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a continuous space to a discrete space. In this paper, a method of feature selection was proposed through two consecutive stages in the first stage, the fuzzy mutual information (FMI) technique is used to determine the most important feature selection of diseases dataset through a fuzzy model that was built based on the data size. In the second stage, the binary gray wolf optimization (BGWO) algorithm is used to determine a specific number of features affecting the process of classification, which came from the first stage. The proposed algorithm, FMI_BGWO, describes efficiency and effectiveness by obtaining a higher classification accuracy and a small number of selected genes compared to other competitor algorithms.

Abstract Read Full Article HTML DOI: 10.29328/journal.apb.1001010 Cite this Article