We describe an imputation method (EACImpute) that is based on an evolutionary algorithm for clustering. This method relies on the assumption that clusters of (partially unknown) data can provide useful information for imputation purposes. Experimental results obtained in 5 data sets illustrate different scenarios in which EACImpute performs similarly to widely used imputation methods, thus becoming eligible to join a pool of methods to be used in practical applications. In particular, imputation methods have been traditionally only assessed by some measures of their prediction capability. Although this evaluation is useful, we here also discuss the influence of imputed values in the classification task. Finally, our empirical results suggest that better prediction results do not necessarily imply in less classification bias.
{"title":"EACImpute: An Evolutionary Algorithm for Clustering-Based Imputation","authors":"J. Silva, Eduardo R. Hruschka","doi":"10.1109/ISDA.2009.86","DOIUrl":"https://doi.org/10.1109/ISDA.2009.86","url":null,"abstract":"We describe an imputation method (EACImpute) that is based on an evolutionary algorithm for clustering. This method relies on the assumption that clusters of (partially unknown) data can provide useful information for imputation purposes. Experimental results obtained in 5 data sets illustrate different scenarios in which EACImpute performs similarly to widely used imputation methods, thus becoming eligible to join a pool of methods to be used in practical applications. In particular, imputation methods have been traditionally only assessed by some measures of their prediction capability. Although this evaluation is useful, we here also discuss the influence of imputed values in the classification task. Finally, our empirical results suggest that better prediction results do not necessarily imply in less classification bias.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114740641","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}
This paper describes and analyzes population size management, which can be used to enhance the efficiency of the extended compact genetic algorithm (ECGA). The ECGA is a selectorecombinative algorithm that requires an adequate sampling to generate a high-quality model of the problem. Population size management decreases the overall running time of the optimization process by splitting the algorithm into two phases: first, it builds a high-quality model of the problem using a large population; second, it generates a smaller population, sampled using the high-quality model, and performs the remaining of the optimization with a reduced population size. The paper shows that for decomposable optimization problems, population size management leads to a significant optimization speedup that decreases the number of evaluations for convergence in ECGA by a factor of 30% to 70% keeping the same accuracy and reliability. Furthermore, the ECGA using PSM presents the same scalability model as the ECGA.
{"title":"Efficiency Enhancement of ECGA Through Population Size Management","authors":"V. V. D. Melo, Thyago Duque, A. Delbem","doi":"10.1109/ISDA.2009.250","DOIUrl":"https://doi.org/10.1109/ISDA.2009.250","url":null,"abstract":"This paper describes and analyzes population size management, which can be used to enhance the efficiency of the extended compact genetic algorithm (ECGA). The ECGA is a selectorecombinative algorithm that requires an adequate sampling to generate a high-quality model of the problem. Population size management decreases the overall running time of the optimization process by splitting the algorithm into two phases: first, it builds a high-quality model of the problem using a large population; second, it generates a smaller population, sampled using the high-quality model, and performs the remaining of the optimization with a reduced population size. The paper shows that for decomposable optimization problems, population size management leads to a significant optimization speedup that decreases the number of evaluations for convergence in ECGA by a factor of 30% to 70% keeping the same accuracy and reliability. Furthermore, the ECGA using PSM presents the same scalability model as the ECGA.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121791297","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}
This paper proposes a novel grammar-based framework of concept representation for randomized search in Relational Learning (RL), namely for Inductive Logic Programming. The utilization of grammars guarantees that the search operations produce syntactically correct concepts and that the background knowledge encoded in the grammar can be used both for directing the search and for restricting the space of possible concepts to relevant candidate concepts (semantically valid concepts). Not only that it enables handling and incorporating the domain knowledge in a declarative fashion, but grammars also make the new approach transparent, flexible, less problem-specific and allow it to be easily used by almost any randomized algorithm within RL. Initial test results suggest that the grammar-based algorithm has strong potential for RL tasks.
{"title":"Grammatical Concept Representation for Randomised Optimisation Algorithms in Relational Learning","authors":"P. Buryan, Jiří Kubalík, Katsumi Inoue","doi":"10.1109/ISDA.2009.156","DOIUrl":"https://doi.org/10.1109/ISDA.2009.156","url":null,"abstract":"This paper proposes a novel grammar-based framework of concept representation for randomized search in Relational Learning (RL), namely for Inductive Logic Programming. The utilization of grammars guarantees that the search operations produce syntactically correct concepts and that the background knowledge encoded in the grammar can be used both for directing the search and for restricting the space of possible concepts to relevant candidate concepts (semantically valid concepts). Not only that it enables handling and incorporating the domain knowledge in a declarative fashion, but grammars also make the new approach transparent, flexible, less problem-specific and allow it to be easily used by almost any randomized algorithm within RL. Initial test results suggest that the grammar-based algorithm has strong potential for RL tasks.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302378","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}
Ocean Project is aimed to realize an Adaptive and Intelligent Web-Based Educational System (AIWBES) working with traditional Learning Management Systems (LMS). It is designed as a collection of open-source libraries (the Omega Framework), so resulting easily customizable and adaptable to the current e-learning platforms. In this new system each course is presented in different ways according to the student’s learning level, through to a combined use of ontologies and fuzzy logic.
{"title":"OCEAN Project A Prototype of AIWBES Based on Fuzzy Ontology","authors":"F. Colleoni, S. Calegari, D. Ciucci, M. Dominoni","doi":"10.1109/ISDA.2009.208","DOIUrl":"https://doi.org/10.1109/ISDA.2009.208","url":null,"abstract":"Ocean Project is aimed to realize an Adaptive and Intelligent Web-Based Educational System (AIWBES) working with traditional Learning Management Systems (LMS). It is designed as a collection of open-source libraries (the Omega Framework), so resulting easily customizable and adaptable to the current e-learning platforms. In this new system each course is presented in different ways according to the student’s learning level, through to a combined use of ontologies and fuzzy logic.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126860204","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}
When designing a neural or fuzzy system, a careful preprocessing of the database is of utmost importance in order to produce a trustable system. In function approximation applications, when a functional relationship between input and output variables is supposed to exist, the presence of data where the similar set of input variables is associated to very different values of the output is not always beneficial for the final system to design. A method is presented which can be used to detect anomalous data, namely non-coherent associations between input and output patterns. This technique, by mean of a comparison between two distance matrix associated to the input and output patterns, is able to detect elements in a dataset, where similar values of input variables are associated to quite different output values. A numerical example and a more complex application in the pre-processing of data coming from an industrial database were presented.
{"title":"A Method to Point Out Anomalous Input-Output Patterns in a Database for Training Neuro-Fuzzy System with a Supervised Learning Rule","authors":"V. Colla, N. Matarese, L. Reyneri","doi":"10.1109/ISDA.2009.202","DOIUrl":"https://doi.org/10.1109/ISDA.2009.202","url":null,"abstract":"When designing a neural or fuzzy system, a careful preprocessing of the database is of utmost importance in order to produce a trustable system. In function approximation applications, when a functional relationship between input and output variables is supposed to exist, the presence of data where the similar set of input variables is associated to very different values of the output is not always beneficial for the final system to design. A method is presented which can be used to detect anomalous data, namely non-coherent associations between input and output patterns. This technique, by mean of a comparison between two distance matrix associated to the input and output patterns, is able to detect elements in a dataset, where similar values of input variables are associated to quite different output values. A numerical example and a more complex application in the pre-processing of data coming from an industrial database were presented.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025052","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}
S. Díaz, Davide Martinetti, Ignacio Montes, S. Montes
The concept of (classical) complete preorder can be characterized in several ways. In previous works we have studied whether complete fuzzy preorders can be characterized by the same properties as in the crisp case. We have proven that this is not usually the case. We have studied five possible characterizations and we have proven that only one still characterizes a fuzzy preorder. In this work we study those properties for additive fuzzy preference structures without incomparability. Despite they do not characterize complete fuzzy preorders, they can be related among them. In this contribution we show their connection when the preference structure does not admit incomparable alternatives.
{"title":"Connection Among Some Characterizations of Complete Fuzzy Preorders","authors":"S. Díaz, Davide Martinetti, Ignacio Montes, S. Montes","doi":"10.1109/ISDA.2009.180","DOIUrl":"https://doi.org/10.1109/ISDA.2009.180","url":null,"abstract":"The concept of (classical) complete preorder can be characterized in several ways. In previous works we have studied whether complete fuzzy preorders can be characterized by the same properties as in the crisp case. We have proven that this is not usually the case. We have studied five possible characterizations and we have proven that only one still characterizes a fuzzy preorder. In this work we study those properties for additive fuzzy preference structures without incomparability. Despite they do not characterize complete fuzzy preorders, they can be related among them. In this contribution we show their connection when the preference structure does not admit incomparable alternatives.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541925","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}
This research proposes efficient calculation methods for the transition matrices in discrete event systems, where the adjacency matrices are represented by directed acyclic graphs. The essence of the research focuses on obtaining the Kleene Star of an adjacency matrix. Previous studies have proposed methods for calculating the longest paths focusing on destination nodes. However, in these methods the chosen algorithm depends on whether the adjacency matrix is sparse or dense. In contrast, this research calculates the longest paths focusing on source nodes. The proposed methods are more efficient than the previous ones, and are attractive in that the efficiency is not affected by the density of the adjacency matrix.
{"title":"Efficient Computation Methods for the Kleene Star in Max-Plus Linear Systems","authors":"H. Goto, M. Kasahara","doi":"10.1109/ISDA.2009.17","DOIUrl":"https://doi.org/10.1109/ISDA.2009.17","url":null,"abstract":"This research proposes efficient calculation methods for the transition matrices in discrete event systems, where the adjacency matrices are represented by directed acyclic graphs. The essence of the research focuses on obtaining the Kleene Star of an adjacency matrix. Previous studies have proposed methods for calculating the longest paths focusing on destination nodes. However, in these methods the chosen algorithm depends on whether the adjacency matrix is sparse or dense. In contrast, this research calculates the longest paths focusing on source nodes. The proposed methods are more efficient than the previous ones, and are attractive in that the efficiency is not affected by the density of the adjacency matrix.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"1122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116073041","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}
In this paper, we discuss the the role of the retrieval component in an TREC style opinion question answering system. Since blog retrieval differs from traditional ad-hoc document retrieval, we need to work on dedicated retrieval methods. In particular we focus on a new query expansion technique to retrieve people’s opinions from blog posts. We propose a combined approach for expanding queries while considering two aspects: finding more relevant data, and finding more opinionative data. We introduce a method to select opinion bearing terms for query expansion based on a chi-squared test and use this new query expansion to combine it in a liner weighting scheme with the original query terms and relevant feedback terms from web. We report our experiments on the TREC 2006 and TREC 2007 queries from the blog retrieval track. The results show that the methods investigated here enhanced mean average precision of document retrieval from 17.91% to 25.20% on TREC 2006 and from 22.28% to 32.61% on TREC 2007 queries.
{"title":"A Combined Query Expansion Technique for Retrieving Opinions from Blogs","authors":"S. Momtazi, Stefan Kazalski, D. Klakow","doi":"10.1109/ISDA.2009.196","DOIUrl":"https://doi.org/10.1109/ISDA.2009.196","url":null,"abstract":"In this paper, we discuss the the role of the retrieval component in an TREC style opinion question answering system. Since blog retrieval differs from traditional ad-hoc document retrieval, we need to work on dedicated retrieval methods. In particular we focus on a new query expansion technique to retrieve people’s opinions from blog posts. We propose a combined approach for expanding queries while considering two aspects: finding more relevant data, and finding more opinionative data. We introduce a method to select opinion bearing terms for query expansion based on a chi-squared test and use this new query expansion to combine it in a liner weighting scheme with the original query terms and relevant feedback terms from web. We report our experiments on the TREC 2006 and TREC 2007 queries from the blog retrieval track. The results show that the methods investigated here enhanced mean average precision of document retrieval from 17.91% to 25.20% on TREC 2006 and from 22.28% to 32.61% on TREC 2007 queries.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122543904","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}
A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.
{"title":"On the Combination of Accuracy and Diversity Measures for Genetic Selection of Bagging Fuzzy Rule-Based Multiclassification Systems","authors":"Krzysztof Trawiński, A. Quirin, O. Cordón","doi":"10.1109/ISDA.2009.123","DOIUrl":"https://doi.org/10.1109/ISDA.2009.123","url":null,"abstract":"A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188715","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}
Davide Magatti, S. Calegari, D. Ciucci, Fabio Stella
An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging, while particularly interesting and significant labeling cases emerged
提出了一种基于层次结构的主题自动标注算法。它的主要成分是一组相似度度量和一组主题标注规则。标记规则专门用于找到给定主题和层次结构之间最一致的标签。层次结构从谷歌Directory服务获得，通过特别开发的软件过程提取，并通过使用OpenOffice English Thesaurus进行扩展。通过使用包含33,801个文档的文档语料库和包含111,795个单词的字典来研究该算法的性能。结果令人鼓舞，同时出现了特别有趣和重要的标签案例
{"title":"Automatic Labeling of Topics","authors":"Davide Magatti, S. Calegari, D. Ciucci, Fabio Stella","doi":"10.1109/ISDA.2009.165","DOIUrl":"https://doi.org/10.1109/ISDA.2009.165","url":null,"abstract":"An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging, while particularly interesting and significant labeling cases emerged","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128627244","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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