Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods

Mining Diversified Shared Decision Tree Sets for Discovering Cross Domain Similarities

This paper studies the problem of mining diversified sets of shared decision trees (SDTs). Given two datasets representing two application domains, an SDT is a decision tree that can perform classification on both datasets and it captures class-based population-structure similarity between the two datasets. Previous studies considered mining just one SDT. The present paper considers mining a small diversified set of SDTs having two properties: (1) each SDT in the set has high quality with regard to “shared” accuracy and population-structure similarity and (2) different SDTs in the set are very different from each other. A diversified set of SDTs can serve as a concise representative of the huge space of possible cross-domain similarities, thus offering an effective way for users to examine/select informative SDTs from that huge space. The diversity of an SDT set is measured in terms of the difference of the attribute usage among the SDTs. The paper provides effective algorithms to mine diversified sets of SDTs. Experimental results show that the algorithms are effective and can find diversified sets of high quality SDTs

Impaired lung function and Health Status in Adult Survivors of Bronchopulmonary Dysplasia

[Abstract]: In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment. As a new technology, Microarray data presents some fresh challenges to scientists since Microarray data contains a large number of genes (around tens thousands) with a small number of samples (around hundreds). Both filter and wrapper gene selection methods aim to select the most informative genes among the massive data in order to reduce the size of the expression database. Gene selection methods are used in both data preprocessing and classification stages. We have conducted some experiments on different existing gene selection methods to preprocess Microarray data for classification by benchmark algorithms SVMs and C4.5. The study suggests that the combination of filter and wrapper methods in general improve the accuracy performance of gene expression Microarray data classification. The study also indicates that not all filter gene selection methods help improve the performance of classification. The experimental results show that among tested gene selection methods, Correlation Coefficient is the best gene selection method for improving the classification accuracy on both SVMs and C4.5 classification algorithms