{"title":"Ensemble classifier with Random Forest algorithm to deal with imbalanced healthcare data","authors":"M. Anbarasi, V. Janani","doi":"10.1109/ICICES.2017.8070752","DOIUrl":"https://doi.org/10.1109/ICICES.2017.8070752","url":null,"abstract":"In day today life, data is generated in massive amount with rapid growth in health care environment. The medical industries have large amount of data sets, for diagnosis purpose and maintain patient's records. The medical researches come with new treatments and medicine every day. But availability of medical datasets is often not balanced in their class labels. The performance of some existing method is poor on imbalanced dataset. So the prediction of disease from imbalanced data becomes difficult to handle. In this proposal Classifier ensemble method (Random Forest algorithm) can be used to overcome existing classifier techniques. Multiple classifier system is more accurate and robust than an existing classifier technique. The ensemble method proves to be very efficient in classification of records from available imbalanced healthcare patient data, as it involves the process of considering opinion from multiple base classifiers, as opposed to the single classifier method. This method gives a very accurate and precise inference, as unrelated data's are removed because of multiple base classifiers. The problems of healthcare dataset especially with some uncertainty can be predicted.","PeriodicalId":134931,"journal":{"name":"2017 International Conference on Information Communication and Embedded Systems (ICICES)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Discovery of Web pattern from web logs files using enhanced graph grammar approach","authors":"M. Anbarasi, B. Vasanthi","doi":"10.1109/ICICES.2017.8070753","DOIUrl":"https://doi.org/10.1109/ICICES.2017.8070753","url":null,"abstract":"Searching useful information without fault from the Web becomes an increasingly difficult task, since the volume of Web data rapidly grows. With the growth rate, unexpected faults of Web service composition may occur in different levels at runtime. These faults are to be identified from Web Log files. The common causes of faults in Web services execution are rectified by fault diagnosis technique. So far, most existing approaches focus on the log content analysis but ignore the structural information and lead to poor performance. To improve the fault classification accuracy, fault classification analysis is carried out in this proposal. The Enhanced graph grammar algorithm is incorporated for identifying different types of fault categories in the form of graph structures.","PeriodicalId":134931,"journal":{"name":"2017 International Conference on Information Communication and Embedded Systems (ICICES)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121870441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}