An efficient gene selection technique for cancer recognition based on neighborhood mutual information

Hu, Qinghua; Pan, Wei; An, Shuang; Ma, Peijun; Wei, Jinmao

doi:10.1007/s13042-010-0008-6

Cited by 103 publications

(43 citation statements)

References 36 publications

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“…Mutating the particle at a certain probability when the diversity decreases, which can restrain the premature convergence of particle and allows the particles to jump out of local optimum in time to find a better solution; on the contrary, that makes the particles have the ability to explore new areas from the early stages of iteration to the end. The calculation formula of particle information entropy [32] is as follows:…”

Section: Information Entropymentioning

confidence: 99%

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Wang

et al. 2016

Computer Assisted Surgery

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The types of kernel function and relevant parameters' selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (PSO): on the one hand, the generalization ability of classifier is effectively enhanced by constructing a mixed kernel function with global kernel function and local kernel function; on the other hand, the accuracy of classification is improved through optimization for related kernel parameters based on information entropy PSO. Compared with PSO-RBF kernel and PSO-mixed kernel, the improved PSO-mixed kernel SVM can effectively improve the classification accuracy through the classification experiment on biomedical datasets, which would not only prove the efficiency of this algorithm, but also show that the algorithm has good practical application value in biomedicine prediction.

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Section: Information Entropymentioning

confidence: 99%

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Wang

et al. 2016

Computer Assisted Surgery

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“…Neighborhood mutual information is defined in the literature [8,9]. information of R and S is defined as…”

Section: Feature Selection Algorithm Based On Neighborhood Mutual Infmentioning

confidence: 99%

“…Neighborhood mutual information (NMI) is an effective measuring method to avoid MI disadvantages. It is constructed by integrating the concept of a neighborhood into Shannon's information theory, and it is a natural generalization of MI in numerical feature spaces [8,9].…”

Section: Introductionmentioning

confidence: 99%

A hybrid ensemble method based on double disturbance for classifying microarray data

Chen

Hui-feng

Zhou

et al. 2015

BME

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Abstract. Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.

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“…Large amount of following studies have successively introduced the concepts of the expansion of Hartley entropy and Shannon entropy [16], relative entropy [17], cumulative residual entropy [18][19][20][21], joint entropy [22,23], conditional entropy [24][25][26], mutual information [27][28][29][30][31][32], cross entropy [33][34][35][36][37][38], fuzzy entropy [15,39], maximum entropy principle [40,41] and minimum cross-entropy principle [42,43], and a series of achievements have been made in these aspects. Zhong makes use of general information functions to unify the methods of describing information metrics with Entropy formulas [4].…”

Section: About the Metrics Of Informationmentioning

confidence: 99%

Objective information theory: A Sextuple model and 9 kinds of metrics

Shen

et al. 2014

2014 Science and Information Conference

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In the contemporary era, the importance of information is undisputed, but there has never been a common understanding of information, nor a unanimous conclusion to the researches on information metrics. Based on the previous studies, this paper analyzes the important achievements in the researches of the properties and metrics of information as well as their main insufficiencies, and discusses the essence and connotation, the mathematical expressions and other basic problems related to information. On the basis of the understanding of the objectivity of information, it proposes the definitions and a Sextuple model of objective information; discusses the basic properties of information, and brings forward the definitions and mathematical expressions of nine kinds of metrics of information, i.e., extensity, detailedness, sustainability, richness, containability, delay, distribution, validity and matchability. Through these, this paper establishes a basic theory frame of Objective Information Theory to support the analysis and research on information and information system systematically and comprehensively.

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An efficient gene selection technique for cancer recognition based on neighborhood mutual information

Cited by 103 publications

References 36 publications

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

A hybrid ensemble method based on double disturbance for classifying microarray data

Objective information theory: A Sextuple model and 9 kinds of metrics

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