Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications最新文献
Qiongyun Zhang, Chi Zhou, Weimin Xiao, Peter C. Nelson
Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the tree structure of different sizes and shapes used in Genetic Programming (GP). As with other GP algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constant generation using Differential Evolution (DE), a real-valued GA robust and efficient at parameter optimization. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variations.
{"title":"Improving gene expression programming performance by using differential evolution","authors":"Qiongyun Zhang, Chi Zhou, Weimin Xiao, Peter C. Nelson","doi":"10.1109/ICMLA.2007.55","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.55","url":null,"abstract":"Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the tree structure of different sizes and shapes used in Genetic Programming (GP). As with other GP algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constant generation using Differential Evolution (DE), a real-valued GA robust and efficient at parameter optimization. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variations.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"28 1","pages":"31-37"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79307268","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}
With the rapid growth of the World Wide Web (www), it becomes a critical issue to design and organize the vast amounts of on-line documents on the web according to their topic. Even for the search engines it is very important to group similar documents in order to improve their performance when a query is submitted to the system. Clustering is useful for taxonomy design and similarity search of documents on such a domain. Similarity is fundamental to many clustering applications on hypertext. In this paper, we will study how measures of similarity are used to cluster a collection of documents on a web site. Most of the document clustering techniques rely on single term analysis of text, such as vector space model. To better group of related documents we propose a new semantic similarity measure. We compare our measure with Wu-Palmer similarity and cosine similarity. Experimental results show that cosine similarity perform better than the semantic similarities. We demonstrate our results on Turkish documents. This is a first study that considers the semantic similarities between Turkish documents.
{"title":"Comparison of semantic and single term similarity measures for clustering turkish documents","authors":"Bülent Yücesoy, Ş. Öğüdücü","doi":"10.1109/ICMLA.2007.52","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.52","url":null,"abstract":"With the rapid growth of the World Wide Web (www), it becomes a critical issue to design and organize the vast amounts of on-line documents on the web according to their topic. Even for the search engines it is very important to group similar documents in order to improve their performance when a query is submitted to the system. Clustering is useful for taxonomy design and similarity search of documents on such a domain. Similarity is fundamental to many clustering applications on hypertext. In this paper, we will study how measures of similarity are used to cluster a collection of documents on a web site. Most of the document clustering techniques rely on single term analysis of text, such as vector space model. To better group of related documents we propose a new semantic similarity measure. We compare our measure with Wu-Palmer similarity and cosine similarity. Experimental results show that cosine similarity perform better than the semantic similarities. We demonstrate our results on Turkish documents. This is a first study that considers the semantic similarities between Turkish documents.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"738 ","pages":"393-398"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91444990","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}
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