Feature selection (FS) and classifier design (CD) are two basic stages in the construction of a classification system. Typically, both tasks have been studied separately in literature. FS aims to remove irrelevant and redundant features whereas CD generates a prediction rule for classifying patterns whose class is unknown. Despite the relationship between FS and CD with radial basis function networks (RBFNs) is noticeable, only some works have addressed FS and CD jointly when constructing RBFNs. This paper presents a methodology for the automatic construction of the RBFN architecture by using two evolutionary algorithms (based on differential evolution, DE) for addressing FS and CD tasks simultaneously. FSDE algorithm evolves a population in order to find a reduced subset of discriminant features. After, each individual generates a subpopulation which evolves to construct the hidden layer of the net via CDDE algorithm. CDDE determines the suitable number of hidden nodes and their parameter. Two real datasets for breast lesion classification were used and the experimental results pointed out that the proposed methodology obtained high classification performance with reduced subsets of features.
{"title":"Evolutionary Approach for Construction of the RBF Network Architecture","authors":"S. Montero-Hernández, W. Gómez-Flores","doi":"10.1109/MICAI.2014.25","DOIUrl":"https://doi.org/10.1109/MICAI.2014.25","url":null,"abstract":"Feature selection (FS) and classifier design (CD) are two basic stages in the construction of a classification system. Typically, both tasks have been studied separately in literature. FS aims to remove irrelevant and redundant features whereas CD generates a prediction rule for classifying patterns whose class is unknown. Despite the relationship between FS and CD with radial basis function networks (RBFNs) is noticeable, only some works have addressed FS and CD jointly when constructing RBFNs. This paper presents a methodology for the automatic construction of the RBFN architecture by using two evolutionary algorithms (based on differential evolution, DE) for addressing FS and CD tasks simultaneously. FSDE algorithm evolves a population in order to find a reduced subset of discriminant features. After, each individual generates a subpopulation which evolves to construct the hidden layer of the net via CDDE algorithm. CDDE determines the suitable number of hidden nodes and their parameter. Two real datasets for breast lesion classification were used and the experimental results pointed out that the proposed methodology obtained high classification performance with reduced subsets of features.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127015190","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}
D. Flores, Claudia M. Gómez, G. Guerra-Rivas, Paulina Tafoya-Romo
Computer simulations are used in situations where the object of study can not be investigated by traditional methods, or not to use more of the test organisms that have already been used. Simulations are useful to test hypotheses or as a support tool to observing other results. The agent based modeling is a powerful and flexible tool that supports simulation experiments in the laboratory, biologists are being used in conjunction with computer specialists as a valuable tool to investigate the properties of biological systems. In this paper, an agent-based model is presented, from a bio-marker enzyme experiment run at the laboratory with mussel Mytilus edulis, marine species of the coast of Baja California, Mexico, exposed to oil dispersant (Nokomis 3-F4) obtained in toxicity tests, also Net Logo simulation tool is used to show the impact over three different tissues (enzymatic activity) of the mussel produced by the oil dispersant.
{"title":"An Agent-Based Model of an Oil Dispersant's Effect on a Marine Species","authors":"D. Flores, Claudia M. Gómez, G. Guerra-Rivas, Paulina Tafoya-Romo","doi":"10.1109/MICAI.2014.40","DOIUrl":"https://doi.org/10.1109/MICAI.2014.40","url":null,"abstract":"Computer simulations are used in situations where the object of study can not be investigated by traditional methods, or not to use more of the test organisms that have already been used. Simulations are useful to test hypotheses or as a support tool to observing other results. The agent based modeling is a powerful and flexible tool that supports simulation experiments in the laboratory, biologists are being used in conjunction with computer specialists as a valuable tool to investigate the properties of biological systems. In this paper, an agent-based model is presented, from a bio-marker enzyme experiment run at the laboratory with mussel Mytilus edulis, marine species of the coast of Baja California, Mexico, exposed to oil dispersant (Nokomis 3-F4) obtained in toxicity tests, also Net Logo simulation tool is used to show the impact over three different tissues (enzymatic activity) of the mussel produced by the oil dispersant.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"469 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627405","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}
Y. Pérez-Pimentel, I. Osuna-Galán, Juan Villegas-Cortez, C. Avilés-Cruz
The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. In this work we provide a new CBIR procedure which works with local texture analysis, and which is developed in a non supervised fashion, clustering the local achieved descriptors and classifying them with the use of a K-means algorithm supported by the genetic algorithm. This method has been deployed in LabVIEW software, programming each part of the procedure in order to implement it in hardware. The results are very promising, reaching up to 90% of recall for natural scene classification.
{"title":"A Genetic Algorithm Applied to Content-Based Image Retrieval for Natural Scenes Classification","authors":"Y. Pérez-Pimentel, I. Osuna-Galán, Juan Villegas-Cortez, C. Avilés-Cruz","doi":"10.1109/MICAI.2014.30","DOIUrl":"https://doi.org/10.1109/MICAI.2014.30","url":null,"abstract":"The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. In this work we provide a new CBIR procedure which works with local texture analysis, and which is developed in a non supervised fashion, clustering the local achieved descriptors and classifying them with the use of a K-means algorithm supported by the genetic algorithm. This method has been deployed in LabVIEW software, programming each part of the procedure in order to implement it in hardware. The results are very promising, reaching up to 90% of recall for natural scene classification.","PeriodicalId":189896,"journal":{"name":"2014 13th Mexican International Conference on Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125804916","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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