An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

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In this paper, we propose apply ABC algorithm in analyzing microarray dataset. In addition, we propose an innovative hybrid classification model, Support Vector Machine (SVM) with ABC algorithm, to measure the classification accuracy for selected genes. We evaluate the performance of the proposed ABC-SVM algorithm by conducting extensive experiments on six binary and multi-class microarrays dataset. Furthermore, we compare our proposed ABC-SVM algorithm with previously known techniques. The experimental results prove that ABC-SVM algorithm is promising approach for solving gene selection and cancer classification problems, and achieves the highest classification accuracy together with the lowest average of selected genes compared to previously suggested methods.

Abstract-Microarray based gene expression profiling has become an important and promising dataset for cancer classification that are used for diagnosis and prognosis purposes. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Furthermore, find accurate gene selection method that reduce the dimensionality and select informative genes is very significant issue in cancer classification area. In literature, there are several gene selection methods for cancer classification using microarray dataset. However, most of them did not concern on identifying minimum number of informative genes with high classification accuracy. Therefore, in our research study we discuss the performance of Bio-Inspired evolutionary gene selection method in cancer classification using microarray dataset. And, we prove that the Bio-Inspired evolutionary gene selection methods have superior classification accuracy with minimum number of selected genes.

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Abstract. Data mining is the process of extracting useful information from a huge amount of data. One of the most common applications of data mining is the use of different algorithms and tools to estimate future events based on previous experiences. In this context, many researchers have been using data mining techniques to support and solve challenges in higher education. There are many challenges facing this level of education, one of which is helping students to choose the right course to improve their success rate. An early prediction of students' grades may help to solve this problem and improve students' performance, selection of courses, success rate and retention. In this paper we use different classification techniques in order to build a performance prediction model, which is based on previous students' academic records. The model can be easily integrated into a recommender system that can help students in their course selection, based on their and other graduated students' grades. Our model uses two of the most recognised decision tree classification algorithms: ID3 and J48. The advantages of such a system have been presented along with a comparison in performance between the two algorithms.

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