{"title":"在医学数据挖掘领域,利用图像处理技术提高自适应分类器的分类精度","authors":"Sneha Chandra, Maneet Kaur","doi":"10.1109/ICGCIOT.2015.7380599","DOIUrl":null,"url":null,"abstract":"Medical Data Mining is one of the most challenging fields of Data Mining. The greatest challenge lies in classifying the diseases with high classification accuracy. In this research work, image processing techniques have been used on the advanced version of our Adaptive Classifier, to generate categories for the attributes of sample medical datasets. The advanced version of our Adaptive Classifier has been generated using the techniques of Clustering Data Mining in conjunction with Classification Data Mining. The proposed approach works upon the sample medical datasets, and compares the results of our Adaptive Classifier with the results of its constituent classifiers. The experimental results generated showed higher classification accuracy for our Adaptive Classifier, which has rightly aroused the curiosity required for further analysis.","PeriodicalId":400178,"journal":{"name":"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Enhancement of classification accuracy of our Adaptive Classifier using image processing techniques in the field of Medical Data Mining\",\"authors\":\"Sneha Chandra, Maneet Kaur\",\"doi\":\"10.1109/ICGCIOT.2015.7380599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical Data Mining is one of the most challenging fields of Data Mining. The greatest challenge lies in classifying the diseases with high classification accuracy. In this research work, image processing techniques have been used on the advanced version of our Adaptive Classifier, to generate categories for the attributes of sample medical datasets. The advanced version of our Adaptive Classifier has been generated using the techniques of Clustering Data Mining in conjunction with Classification Data Mining. The proposed approach works upon the sample medical datasets, and compares the results of our Adaptive Classifier with the results of its constituent classifiers. The experimental results generated showed higher classification accuracy for our Adaptive Classifier, which has rightly aroused the curiosity required for further analysis.\",\"PeriodicalId\":400178,\"journal\":{\"name\":\"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICGCIOT.2015.7380599\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGCIOT.2015.7380599","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhancement of classification accuracy of our Adaptive Classifier using image processing techniques in the field of Medical Data Mining
Medical Data Mining is one of the most challenging fields of Data Mining. The greatest challenge lies in classifying the diseases with high classification accuracy. In this research work, image processing techniques have been used on the advanced version of our Adaptive Classifier, to generate categories for the attributes of sample medical datasets. The advanced version of our Adaptive Classifier has been generated using the techniques of Clustering Data Mining in conjunction with Classification Data Mining. The proposed approach works upon the sample medical datasets, and compares the results of our Adaptive Classifier with the results of its constituent classifiers. The experimental results generated showed higher classification accuracy for our Adaptive Classifier, which has rightly aroused the curiosity required for further analysis.