{"title":"基于二进制编码遗传算法和极限学习机的帕金森病分类","authors":"V. Sachnev, Hyoung-Joong Kim","doi":"10.1109/ISSNIP.2014.6827649","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine\",\"authors\":\"V. Sachnev, Hyoung-Joong Kim\",\"doi\":\"10.1109/ISSNIP.2014.6827649\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.\",\"PeriodicalId\":269784,\"journal\":{\"name\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSNIP.2014.6827649\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSNIP.2014.6827649","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine
In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.