Minimum redundancy feature selection from microarray gene expression data

Abstract

Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy -maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.

Sensor Selection for IT Infrastructure Monitoring

“…We use the systematic, well-tunable mRMR algorithm for variable selection. It is also based on mutual information and has been shown to scale well for large problem spaces [10,11].…”

Set Covering-based Feature Selection of Large-scale Omics Data

“…; 유전자 발현 데 이터 분석 [11,12,15]; DNA 메틸화 데이터 분석 [18,27]; 단백질 데이터 분석 [24,26] 해 그동안의 기법들은 t-test [16], wilcoxon ranksum test [22]와 같이 각 변수들을 한 개씩 독립적 으로 선택하는 것이 대부분이었다 [21]. 그 외에는 동시에 여러 개의 변수를 고려하는 방법으로 SVM 을 이용한 연구와(예 : [13,25] 의 알고리즘을 사용하였다.…”

Incremental wrapper-based gene selection from microarray data for cancer classification

“…In the second one, a fast correlation-based filter is implemented (FCBF), where M is formed by only one attribute, and gradually eliminates redundant attributes with respect to M from the first to the final attribute of an ordered list. Other methods based on relevance and redundancy concepts can be found in (Guyon et al, 2002;Ding and Peng, 2003).…”

“…This combinatorial problem is solved in a greedy fashion at each iteration of training by removing the input dimension that decreases the margin the least until only r input dimensions remain (this is known as backward selection). Ding and Peng (2003) have used mutual information for gene selection that has maximum relevance with minimal redundancy by solving a simple two-objective optimization. Xing et al (2001) proposed a hybrid of filter and wrapper approaches to feature selection.…”

“…Analysis of microarray data presents unprecedented opportunities and challenges for data mining in areas such as gene clustering (Alon et al, 1999), sample clustering and class discovery (Alon et al, 1999;Golub et al, 1999), sample classification (Alizadeh et al, 2000;Golub et al, 1999) and gene selection (Ding and Peng, 2003;Inza et al, 2004;Yu and Liu, 2004b;Guyon et al, 2002;Xing et al, 2001). In this work, we address the gene selection issue under a classification framework.…”