Recognition of Multiple Imbalanced Cancer Types Based on DNA Microarray Data Using Ensemble Classifiers

Abstract

DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.

An insight into imbalanced Big Data classification: outcomes and challenges

“…3, where we show a scatter plot for the training data of the yeast4 problem from KEEL imbalanced dataset repository 6 [69] (attributes mcg vs. gvh) with only a 10 % of the original instances (Fig. 3a) and with the entire dataset (Fig.…”

“…This case study may be due to rarity of occurrence of a given concept, or even because of some restrictions during the gathering of data for a particular class. In this sense, class imbalance is ubiquitous and prevalent in several applications such as microarray research [6], medical diagnosis [7], oil-bearing of reservoir recognition [8], or intrusion detection systems [9].…”

Improving pattern classification of DNA microarray data by using PCA and logistic regression

“…Some authors have conducted comparative studies, like the ones reported in [35,43,56]. Authors such as [63] suggest that SVM behave well in general for this problems, although it is important to consider that such method was originally developed for bi-class problems, and applying SVMs to multi-class classification implies a number of additional steps, that might not be easy to accomplish, as described in [54].…”

“…In [63], the authors deal with multi-class imbalance classification problems. The method divides multiclass problems into multiple binary-class problems.…”

Artificial neural network classification of microarray data using new hybrid gene selection method

“…Generalised Radial Basis Function neural networks were used for improving prediction accuracy in gene classification using three filter techniques for feature selection of microarray data (Fernández-Navarro et al, 2012). Yu et al (2013a) performed a feature selection in DNA microarrays using an ensemble learning technique. In addition, they used an algorithm that converts a multi-class problem into multiple binary classes to reduce the complexity of the problem.…”