Improved Technique for Cancerous Gene Selection Based on PSO and BW ratio

Saqib, Nazmus; Aman, Farhat

dc.contributor.author	Saqib, Nazmus
dc.contributor.author	Aman, Farhat
dc.date.accessioned	2021-09-10T03:43:10Z
dc.date.available	2021-09-10T03:43:10Z
dc.date.issued	2013-11-15
dc.identifier.citation	[1] Farzana Kabir Ahmad, Prof. Dr. Safaai Deri, Assoc. Prof. Dr. Norita Md. Norwawi, Prof. Dr. Nor Hayati Othman "A Review of Feature Selection Techniques via Gene Expression Pro les",2008. [2] Chung-Jui Tu, Li-Yeh Chuang, Jun-Yang Chang, and Cheng-Hong Yang "Feature Selection using PSO-SVM",2009. [3] Zhi-Hui Zhan, Jun Zhang, Yun Li, Henry Shu-Hung Chung "Adaptive Particle Swarm Optimization",2009. [4] Qinghai Bai "Analysis of Particle Swarm Optimization Algorithm",2010. [5] Haider Banka, Suresh Dara "Feature Selection and Classi cation for GeneExpression Data using Evolutionary Computation",2012. [6] Baiyi Xie, Shihong Chen, Feng Liu "Biclustering of Gene Expression Data Using PSO-GA Hybrid",2007. [7] Li-Yeh Chuang, Hua-Fang Jhang, Cheng-Hong Yang, "Feature Selection using Complementary Particle Swarm Optimization for DNA Microarray Data" [8] Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiyati Yuhaniz "Particle Swarm Optimization: Technique, System and Challenges",2013. [9] V.Selvi, Dr.R.UMARANI "Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques",2010. [10] Kwang Y. Lee, Jong-Bae Park "Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages",2006. [11] Sandrine Dudoit, Jane Fridlyand, and Terence P. Speed "Comparison of Discrimination Methods for the Classication of Tumors Using Gene Expression Data",2012. 26	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/934
dc.description	Supervised by Prof. Dr. M.A. Mottalib Head of the Department, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT). Shaikh Jeeshan Kabeer, Co-supervisor, Lecturer, Department of Computer Science and Engineering (CSE), Islamic University of Technology(IUT).	en_US
dc.description.abstract	The relentless development of Microarray technology have meant that the dimen- sionality of data that is produced by the Microarray chips have increased many folds over the years. Recognition of patterns and other subsequent analysis from the thousands of gene expression values is particularly di cult and primary role of an e ective feature selection is to simplify this task. Removal of less informative genes helps to alleviate the e ects of noise and redundancy, and simpli es the task of disease classi cation and prediction of medical conditions such as cancer. In this study the shortcoming of the current GA based approach for feature selection has been improved. A lter and wrapper models are put to use to take advantage of the facilities that each provides. As lter method exhibits some limitations, in this study an approach to ltering (BW ratio) has been employed. As a wrapper approach Particle Swarm Optimization (PSO) has been proposed.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Improved Technique for Cancerous Gene Selection Based on PSO and BW ratio	en_US
dc.type	Thesis	en_US