title: Linear regression-based feature selection for microarray data classification.
abstract: Predicting the class of gene expression profiles helps improve the diagnosis and treatment of diseases. Analysing huge gene expression data otherwise known as microarray data is complicated due to its high dimensionality. Hence the traditional classifiers do not perform well where the number of features far exceeds the number of samples. A good set of features help classifiers to classify the dataset efficiently. Moreover, a manageable set of features is also desirable for the biologist for further analysis. In this paper, we have proposed a linear regression-based feature selection method for selecting discriminative features. Our main focus is to classify the dataset more accurately using less number of features than other traditional feature selection methods. Our method has been compared with several other methods and in almost every case the classification accuracy is higher using less number of features than the other popular feature selection methods.
authors.forename: Md
authors.initials: M
authors.lastname: Abid Hasan
authors.forename: Md Kamrul
authors.initials: MK
authors.lastname: Hasan
authors.forename: M
authors.initials: M
authors.lastname: Abdul Mottalib
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016014:Linear Models; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
chemical_list: D009363:Neoplasm Proteins