{"title":"An Instance Based Learning Model for Classification in Data Streams with Concept Change","authors":"D. Torres, J. Aguilar-Ruiz, Yanet Rodríguez","doi":"10.1109/MICAI.2012.22","DOIUrl":"https://doi.org/10.1109/MICAI.2012.22","url":null,"abstract":"Mining data streams has attracted the attention of the scientific community in recent years with the development of new algorithms for processing and sorting data in this area. Incremental learning techniques have been used extensively in these issues. A major challenge posed by data streams is that their underlying concepts can change over time. This research delves into the study of applying different techniques of classification for data streams, with a proposal based on similarity including a new methodology for detect and treatment of concept change. Previous experimentation are conduced with the model because it have some parameters to be tuned. A comparative statistical analysis are presented, that shows the performance of the proposed algorithm.","PeriodicalId":348369,"journal":{"name":"2012 11th Mexican International Conference on Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129298956","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":"Recognizing Textual Entailment with a Semantic Edit Distance Metric","authors":"Miguel Rios, Alexander Gelbukh","doi":"10.1109/MICAI.2012.29","DOIUrl":"https://doi.org/10.1109/MICAI.2012.29","url":null,"abstract":"We present a Recognizing Textual Entailment(RTE) system based on different similarity metrics. The metrics used are string-based metrics and the Semantic Edit Distance Metric, which is proposed in this paper to address limitations of known semantic-based metrics and to support the decisions made by a simple method based on lexical similarity metrics.We add the scores of the metrics as features for a machine learning algorithm. The performance of our system is comparable with the average performance of the Recognizing Textual Entailment Challenges, though lower than that of the state-of-the-art methods.","PeriodicalId":348369,"journal":{"name":"2012 11th Mexican International Conference on Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129344823","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}