The Hybrid BPSO/BLDA Model for Gene Selection and Cancer Classification based on Microarray Data
محل انتشار: چهاردهمین کنفرانس سیستم های فازی ایران
سال انتشار: 1394
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 562
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شناسه ملی سند علمی:
ICFUZZYS14_061
تاریخ نمایه سازی: 21 اردیبهشت 1397
چکیده مقاله:
Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. Therefore, gene selection techniques should be utilized before classification to remove the non-informative genes from the microarray data. In this paper, a new method is proposed for gene selection based on hybrid Binary Particle Swarm Optimization (BPSO) and Bayesian Linear Discriminant Analysis (BLDA) in order to classify a large scale of microarray data. The proposed algorithm is applied on four cancer datasets and its results are compared with other existing methods. The results illustrate that the proposed algorithm has higher accuracy and validity in comparison to other existing methods and is able to select the small subset of informative genes in order to increase the classification accuracy.
کلیدواژه ها:
Gene expression ، Binary Particle Swarm Optimization ، Bayesian Linear Discriminant Analysis ، Classification ، Gene selection
نویسندگان
Mahsa Joroughi
Genomic Signal Processing Lab, Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Mousa Shamsi
Genomic Signal Processing Lab, Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Hamidreza Saberkari
Genomic Signal Processing Lab, Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Mohammad Hossein Sedaaghi
Genomic Signal Processing Lab, Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran