A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set

Abstract

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality of the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. This paper provides a comparison between dimension reduction technique, namely Partial Least Squares (PLS)method and a hybrid feature selection scheme, and evaluates the relative performance of four different supervised classification procedures such as Radial Basis Function Network (RBFN), Multilayer Perceptron Network (MLP), Support Vector Machine using Polynomial kernel function(Polynomial-SVM) and Support Vector Machine using RBF kernel function (RBF-SVM) incorporating those methods. Experimental results show that the Partial Least-Squares(PLS) regression method is an appropriate feature selection method and a combined use of different classification and feature selection approaches makes it possible to construct high performance classification models for microarray data.

A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

“…Wang, 2005. SVMs regarding its high accuracy and flexibility in modeling various sources of data in the area of computational biology, an aspect of bioinformatic were further stated in the work of Dash et al, 2012. The importance of SVM classifier and its applications cannot be over emphasized.…”

Data Visualization and its Proof by Compactness Criterion of Objects of Classes

“…A detailed description of the algorithm for selecting the latent features by (9) and (10) can be found in [11]. proposed in [12], makes it possible to use the results of the described algorithm as a decision rule for recognition.…”

“…In [8] a genetic algorithm (GA)-based features selection to improve the accuracy of medical data classification is proposed. In [9] developed a novel feature selection technique based on the Partial Least Squares (PLS). PLS aims to obtain a low dimensional approximation of a matrix that is ‗as close as possible' to a given vector.…”

Machine Learning Based Approaches for Cancer Classification Using Gene Expression Data

“…Tripathy developed new research on Hybrid classification Data Mining technique used for improving the accuracy of microarray data set [41] . This recent work presented a comparison between dimension reduction technique, Hybrid feature selection scheme and a Partial Least Squares (PLS) method evaluates the comparative performance of four different supervised classification methods such as Multilayer Perceptron Network (MLP), Radial Basis Function Network (RBFN), Support Vector Machine with RBF kernel function (SVM-RBF) and Support Vector Machine by using Polynomial kernel function (Polynomial-SVM).…”