Redundancy based feature selection for microarray data

Yu, Lei; Liu, Huan

doi:10.1145/1014052.1014149

Cited by 199 publications

(103 citation statements)

References 20 publications

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“…To this end, Koller and Sahami [17] developed an optimal gene selection method called Markov blanket filtering which models feature dependencies and can eliminate redundant genes. Further to this method, Yu and Liu [31] proposed the Redundancy Based Filter(RBF) method, which is able to deal with redundant problems. Favorable results have been achieved.…”

Section: Gene Selection Methodsmentioning

confidence: 99%

On the Effectiveness of Gene Selection for Microarray Classification Methods

Zhang

et al. 2010

Intelligent Information and Database Systems

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Abstract. Microarray data usually contains a high level of noisy gene data, the noisy gene data include incorrect, noise and irrelevant genes. Before Microarray data classification takes place, it is desirable to eliminate as much noisy data as possible. An approach to improving the accuracy and efficiency of Microarray data classification is to make a small selection from the large volume of high dimensional gene expression dataset. An effective gene selection helps to clean up the existing Microarray data and therefore the quality of Microarray data has been improved. In this paper, we study the effectiveness of the gene selection technology for Microarray classification methods. We have conducted some experiments on the effectiveness of gene selection for Microarray classification methods such as two benchmark algorithms: SVMs and C4.5. We observed that although in general the performance of SVMs and C4.5 are improved by using the preprocessed datasets rather than the original data sets in terms of accuracy and efficiency, while an inappropriate choice of gene data can only be detrimental to the power of prediction. Our results also implied that with preprocessing, the number of genes selected affects the classification accuracy.

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Section: Gene Selection Methodsmentioning

confidence: 99%

On the Effectiveness of Gene Selection for Microarray Classification Methods

Zhang

et al. 2010

Intelligent Information and Database Systems

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“…To eliminate this problem, Koller and Sahami [13] developed an optimal gene selection method called Markov blanket filtering which can remove redundant genes. Based on this method, Yu and Liu [26] proposed the Redundancy Based Filter(RBF) method to deal with redundant problems and the results are very promising.…”

Section: Gene Selection Methodsmentioning

confidence: 99%

Combined Gene Selection Methods for Microarray Data Analysis

Wang

et al. 2006

Lecture Notes in Computer Science

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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.

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“…This measure is simply the expected reduction in entropy caused by partitioning the data according to this feature, so-called Information Gain [16]. Assuming a given set of microarray gene expression data M , the information gain of a gene i is defined as:…”

Section: A Information Gainmentioning

confidence: 99%

Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis

Wang

Palade

Xu³

2006

2006 International Symposium on Evolving Fuzzy Systems

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Abstract-A Neuro-Fuzzy Ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some current most widely used approaches, NeuroFuzzy(NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data.

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Redundancy based feature selection for microarray data

Cited by 199 publications

References 20 publications

On the Effectiveness of Gene Selection for Microarray Classification Methods

On the Effectiveness of Gene Selection for Microarray Classification Methods

Combined Gene Selection Methods for Microarray Data Analysis

Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis

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