{"title":"通过集成神经网络改进分类","authors":"Khobaib Zaamout, John Z. Zhang","doi":"10.1109/ICNC.2012.6234540","DOIUrl":null,"url":null,"abstract":"We consider using neural networks as an ensemble technique to improve classification accuracy. Neural networks are among the best techniques used for classification. In this work, we make use of ensemble approach to combine individual neural networks' outputs by another neural network. Furthermore, we propose to include original data as additional inputs for the ensemble neural network. The effectiveness of our proposed approach is demonstrated through a series of experiments on real and synthetic datasets.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Improving classification through ensemble neural networks\",\"authors\":\"Khobaib Zaamout, John Z. Zhang\",\"doi\":\"10.1109/ICNC.2012.6234540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider using neural networks as an ensemble technique to improve classification accuracy. Neural networks are among the best techniques used for classification. In this work, we make use of ensemble approach to combine individual neural networks' outputs by another neural network. Furthermore, we propose to include original data as additional inputs for the ensemble neural network. The effectiveness of our proposed approach is demonstrated through a series of experiments on real and synthetic datasets.\",\"PeriodicalId\":404981,\"journal\":{\"name\":\"2012 8th International Conference on Natural Computation\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 8th International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2012.6234540\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 8th International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2012.6234540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving classification through ensemble neural networks
We consider using neural networks as an ensemble technique to improve classification accuracy. Neural networks are among the best techniques used for classification. In this work, we make use of ensemble approach to combine individual neural networks' outputs by another neural network. Furthermore, we propose to include original data as additional inputs for the ensemble neural network. The effectiveness of our proposed approach is demonstrated through a series of experiments on real and synthetic datasets.