Patent classification system using a new hybrid genetic algorithm support vector machine

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.

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Construction and Practice of the Optimal Smooth Semi-Supervised Support Vector Machine

Journal of Systems Science and Information ◽

10.1515/jssi-2015-0398 ◽

2015 ◽

Vol 3 (5) ◽

pp. 398-410 ◽

Cited By ~ 1

Author(s):

Xiaodan Zhang ◽

Ang Li ◽

Pan Ran

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Unconstrained Optimization ◽

Numerical Experiments ◽

Spline Function ◽

Optimization Problem ◽

Hybrid Genetic Algorithm ◽

Experimental Results ◽

Support Vector ◽

Unconstrained Optimization Problem

AbstractThe standard semi-supervised support vector machine (S3VM) is an unconstrained optimization problem of non-convex and non-smooth, so many smooth methods are applied for smoothing S3VM. In this paper, a new smooth semi-supervised support vector machine (SS3VM) model , which is based on the biquadratic spline function, is proposed. And, a hybrid Genetic Algorithm (GA)/ SS3VM approach is presented to optimize the parameters of the model. The numerical experiments are performed to test the efficiency of the model. Experimental results show that generally our optimal SS3VM model outperforms other optimal SS3VM models mentioned in this paper.

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Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10110766 ◽

2021 ◽

Vol 10 (11) ◽

pp. 766

Author(s):

Xishihui Du ◽

Kefa Zhou ◽

Yao Cui ◽

Jinlin Wang ◽

Shuguang Zhou

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Hybrid Genetic Algorithm ◽

Geochemical Data ◽

Support Vector ◽

Adaptive Optimization ◽

Spatial Efficiency ◽

Svm Model ◽

Mineral Prospectivity ◽

Optimization Search

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

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