Hybrid Gene Selection Framework using Adaptive Wrapper and Filtering Techniques

Islam, Md Anisul; Mottalib, Md Mozaharul

dc.contributor.author	Islam, Md Anisul
dc.contributor.author	Mottalib, Md Mozaharul
dc.date.accessioned	2021-09-16T05:00:44Z
dc.date.available	2021-09-16T05:00:44Z
dc.date.issued	2014-10-15
dc.identifier.citation	[1] Li-ye-chuang et al.(2012) A hybrid BPSO-CGA Approach for Gene Selection and Classification of Microarray Data JOURNAL OF COMPUTATIONAL BIOLOGY Volume 19, Number 1,2012 [2] Juana Canul-Reich et al. (2008) Feature Selection for Microarray Data by AUC [3] John Quackenbush (2001) Computational Genetics: Computational analysis of microarray data [4] Cosmin Lazar (2012) A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis [5] RuichuCai et al (2010) A New Hybrid Method for Gene Selection [6] Wei Zhao et al. (2011) A Novel Framework for Gene Selection [7] Kelly Fleetwood et al. (2010) an Introduction to Differential Evolution [8] Wei-Neng Chen et al. (2010) A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems [9] L.-Y. Chuang et al. (2010) Correlation-based Gene Selection and Classification Using Taguchi-BPSO [10] Mohd Saberi Mohamad et al. (2009) Particle swarm optimization for gene selection in classifying cancer classes. In Artif Life Robotics (2009) 14:16– 19 [11] Sheng Ding-(2009) Feature Selection based F-score and ACO Algorithmin Support Vector Machine [12] Shutao Li et al. (2008) Gene selection using hybrid particle swarm optimization and genetic algorithm [13] Mojtaba Ahmadieh Khanesar et al. (2007) A Novel Binary Particle Swarm Optimization. In 15th Mediterranean Conference on Control and Automation, Athens, Greece. [14] Xueming Yang et al. (2007) A modified particle swarm optimizer with dynamic adaptation [15] R. K. Agrawal, et al. (2007) a Hybrid Approach for Selection of Relevant Features for Microarray Datasets 30 [16] Qi Shen et al. (2006) a combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification [17] Chung-Jui Tu et al. (2006) Feature Selection using PSO-SVM [18] Xian Xu et al. (2006) Boost Feature Subset Selection: A New Gene Selection Algorithm for Microarray Dataset [19] Sergio Ledesma et al. (2008) Feature Selection Using Artificial Neural Networks [20] Akbar Rahideh et al. (2011) Cancer Classification Using Clustering Based Gene Selection and Artificial Neural Networks [21] Md. Monirul Kabir et al (2010) a new wrapper feature selection approach using neural network [22] Backstorm, L. et al. (2006) C2FS: An Algorithm for Feature Selection in Cascade Neural Networks [23] Vitaly Schetinin et al. (2003) A Learning Algorithm for Evolving Cascade Neural Networks	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1000
dc.description	Supervised by Prof. Dr. M. A. Mottalib, Head of the Department, and Co-Supervisor, Shaikh Jeeshan Kabeer, Lecturer, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	Analysing the thousands of gene expression values is a difficult task due to the curse of dimensionality of data produced by Microarray chips. Primary role of an effective feature selection model is to simplify this task. To simplify the task of disease classification and predicting cancer, feature selection plays a vital role through removing less informative genes. In this study, we propose a hybrid approach to gene selection using adaptive filter and adaptive wrapper approach. As filter method exhibits some limitations, an adaptive form of filtering has been employed that iteratively selects genes in each iteration and emphasizes on the misclassified samples and in subsequent iteration tries to find out the effective genes for misclassified samples. This approach performs better than traditional filter methods as it focuses on its weaknesses. In gene selection, Artificial Neural Networks (ANN) are mostly used as a classifier. In this study, adaptive ANN has been used as an internal wrapper. This helps to generate a better subset of genes. The proposed hybrid approach is applied on leukaemia, colon and lung cancer benchmarked datasets. Better result has been found compared to other well-known approaches.	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	Hybrid Gene Selection Framework using Adaptive Wrapper and Filtering Techniques	en_US
dc.type	Thesis	en_US