Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121789
J. Vidal, M. Lama, Estefanía Otero-García, Alberto Bugarín-Diz
In this paper we present an approach for improving a specific class of semantic annotation, that relates a term of the document with a (sub)tree of the ontology, instead of linking a term with a single concept of the ontology. An important part of this class of annotation is filtering the relevant (sub)nodes and relations, because the returned graph should only contain relevant information, that is, nodes that are truly related with the topics of the document. In addition, we consider that the relevance of nodes vary depending on if the node is a branch or a leaf, that is, if the node has links to other nodes or it is a text-based description. This paper focuses on the relevance of branch nodes, which is calculated from the relevance of its links, since leaf nodes relevance is usually estimated by similarity metrics. Specifically, our approach incises in learning (through a genetic algorithm) and assigning the most appropriate weights to these links in order to reduce the precision/recall curve of the annotation process. The results show that our solution is viable and outperforms the state of the art approaches.
{"title":"An evolutionary approach for learning the weight of relations in linked data","authors":"J. Vidal, M. Lama, Estefanía Otero-García, Alberto Bugarín-Diz","doi":"10.1109/ISDA.2011.6121789","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121789","url":null,"abstract":"In this paper we present an approach for improving a specific class of semantic annotation, that relates a term of the document with a (sub)tree of the ontology, instead of linking a term with a single concept of the ontology. An important part of this class of annotation is filtering the relevant (sub)nodes and relations, because the returned graph should only contain relevant information, that is, nodes that are truly related with the topics of the document. In addition, we consider that the relevance of nodes vary depending on if the node is a branch or a leaf, that is, if the node has links to other nodes or it is a text-based description. This paper focuses on the relevance of branch nodes, which is calculated from the relevance of its links, since leaf nodes relevance is usually estimated by similarity metrics. Specifically, our approach incises in learning (through a genetic algorithm) and assigning the most appropriate weights to these links in order to reduce the precision/recall curve of the annotation process. The results show that our solution is viable and outperforms the state of the art approaches.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736448","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121799
Diana Trandabat
This papers presents a semantic role labeling system for Romanian texts. The semantic labeling system was developed using PASRL, a platform for supervised learning techniques. The developed platform tests several classifiers on different sub-problems of the SRL task (Predicate Identification, Predicate Sense Identification, Sense Identification, Argument Identification), chooses the ones with the greatest performance and returns a Semantic Role Labeling System (a sequence of trained models to run on new data).
{"title":"Mining Romanian texts for semantic knowledge","authors":"Diana Trandabat","doi":"10.1109/ISDA.2011.6121799","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121799","url":null,"abstract":"This papers presents a semantic role labeling system for Romanian texts. The semantic labeling system was developed using PASRL, a platform for supervised learning techniques. The developed platform tests several classifiers on different sub-problems of the SRL task (Predicate Identification, Predicate Sense Identification, Sense Identification, Argument Identification), chooses the ones with the greatest performance and returns a Semantic Role Labeling System (a sequence of trained models to run on new data).","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130835755","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121775
D. Álvarez, R. Hornero, J. Victor Marcos, T. Penzel, F. Campo, N. Wessel
This study focused on prospectively testing diagnostic performance of different logistic regression (LR) models in the context of sleep apnea hypopnea syndrome (SAHS) detection from blood oxygen saturation (SaO2) recordings. Feature extraction, selection and classification procedures were applied. Time, frequency, linear and nonlinear analyses were carried out to compose the initial feature set. Forward stepwise logistic regression (FSLR) was applied for feature selection. LR was used to measure performance classification of single features and an optimum feature subset from FSLR. A training set composed of 148 recordings from patients suspected of suffering from SAHS was used to obtain LR models, which were further validated on a dataset composed of 50 recordings from normal healthy subjects and 21 recordings from SAHS patients, all derived from an independent sleep unit. Diagnostic performance of one-feature LR models from oximetry in the training set significantly changed on further assessments in the test set. On the other hand, FSLR provided a more general LR model in the context of SAHS, which reached an accuracy of 89.7% on the training set and 87.3% on the test set.
{"title":"Prospective evaluation of logistic regression models from overnight oximetry to assist in sleep apnea diagnosis","authors":"D. Álvarez, R. Hornero, J. Victor Marcos, T. Penzel, F. Campo, N. Wessel","doi":"10.1109/ISDA.2011.6121775","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121775","url":null,"abstract":"This study focused on prospectively testing diagnostic performance of different logistic regression (LR) models in the context of sleep apnea hypopnea syndrome (SAHS) detection from blood oxygen saturation (SaO2) recordings. Feature extraction, selection and classification procedures were applied. Time, frequency, linear and nonlinear analyses were carried out to compose the initial feature set. Forward stepwise logistic regression (FSLR) was applied for feature selection. LR was used to measure performance classification of single features and an optimum feature subset from FSLR. A training set composed of 148 recordings from patients suspected of suffering from SAHS was used to obtain LR models, which were further validated on a dataset composed of 50 recordings from normal healthy subjects and 21 recordings from SAHS patients, all derived from an independent sleep unit. Diagnostic performance of one-feature LR models from oximetry in the training set significantly changed on further assessments in the test set. On the other hand, FSLR provided a more general LR model in the context of SAHS, which reached an accuracy of 89.7% on the training set and 87.3% on the test set.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129167785","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121643
M. Cimino, B. Lazzerini, F. Marcelloni, G. Castellano, A. Fanelli, M. Torsello
Situation-aware service recommendation for mobile devices is aimed at proactively pushing personalized suggestions to users, presenting them unseen or unknown services. A challenging area in the field is that of recommendation schemes emerging from users' collective behavior. When we consider a mobile user, for instance, the recommendation process can be based on social events that can arise from collective positioning information. In this scenario, we discuss a collaborative multi-agent scheme for event detection, in which fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. More specifically, the first level of information processing is managed by marking agents leaving marks in the environment which are associated with users' positioning. The accumulation of marks enables a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, a fuzzy inference level, managed by situation agents, deduces user situations from the underlying events.
{"title":"A collaborative situation-aware scheme for mobile service recommendation","authors":"M. Cimino, B. Lazzerini, F. Marcelloni, G. Castellano, A. Fanelli, M. Torsello","doi":"10.1109/ISDA.2011.6121643","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121643","url":null,"abstract":"Situation-aware service recommendation for mobile devices is aimed at proactively pushing personalized suggestions to users, presenting them unseen or unknown services. A challenging area in the field is that of recommendation schemes emerging from users' collective behavior. When we consider a mobile user, for instance, the recommendation process can be based on social events that can arise from collective positioning information. In this scenario, we discuss a collaborative multi-agent scheme for event detection, in which fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. More specifically, the first level of information processing is managed by marking agents leaving marks in the environment which are associated with users' positioning. The accumulation of marks enables a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, a fuzzy inference level, managed by situation agents, deduces user situations from the underlying events.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125384938","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121724
Silvia Acid, L. M. D. Campos, Moisés Fernández
Feature subset selection is increasingly becoming an important preprocessing step within the field of automatic classification. This is due to the fact that the domain problems currently considered contain a high number of variables, and some kind of dimensionality reduction becomes necessary, in order to make the classification task approachable. In this paper we make an experimental comparison between a state-of-the-art method for feature selection, namely minimum Redundancy Maximum Relevance, and a recently proposed method for learning Markov boundaries based on searching for Bayesian network structures in constrained spaces using standard scoring functions.
{"title":"Minimum redundancy maximum relevancy versus score-based methods for learning Markov boundaries","authors":"Silvia Acid, L. M. D. Campos, Moisés Fernández","doi":"10.1109/ISDA.2011.6121724","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121724","url":null,"abstract":"Feature subset selection is increasingly becoming an important preprocessing step within the field of automatic classification. This is due to the fact that the domain problems currently considered contain a high number of variables, and some kind of dimensionality reduction becomes necessary, in order to make the classification task approachable. In this paper we make an experimental comparison between a state-of-the-art method for feature selection, namely minimum Redundancy Maximum Relevance, and a recently proposed method for learning Markov boundaries based on searching for Bayesian network structures in constrained spaces using standard scoring functions.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125558916","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121817
M. Pérez-Ortiz, Pedro Antonio Gutiérrez, C. García-Alonso, L. Salvador-Carulla, J. Salinas-Pérez, C. Hervás‐Martínez
In this paper we apply and test a recent ordinal algorithm for classification (Kernel Discriminant Learning Ordinal Regression, KDLOR), in order to recognize a group of geographically close spatial units with a similar prevalence pattern significantly high (or low), which are called hot-spots (or cold-spots). Different spatial analysis techniques have been used for studying geographical distribution of a specific illness in mental health-care because it could be useful to organize the spatial distribution of health-care services. Ordinal classification is used in this problem because the classes are: spatial unit with depression, spatial unit which could present depression and spatial unit where there is not depression. It is shown that the proposed method is capable of preserving the rank of data classes in a projected data space for this database. In comparison to other standard methods like C4.5, SVMRank, Adaboost, and MLP nominal classifiers, the proposed KDLOR algorithm is shown to be competitive.
{"title":"Ordinal classification of depression spatial hot-spots of prevalence","authors":"M. Pérez-Ortiz, Pedro Antonio Gutiérrez, C. García-Alonso, L. Salvador-Carulla, J. Salinas-Pérez, C. Hervás‐Martínez","doi":"10.1109/ISDA.2011.6121817","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121817","url":null,"abstract":"In this paper we apply and test a recent ordinal algorithm for classification (Kernel Discriminant Learning Ordinal Regression, KDLOR), in order to recognize a group of geographically close spatial units with a similar prevalence pattern significantly high (or low), which are called hot-spots (or cold-spots). Different spatial analysis techniques have been used for studying geographical distribution of a specific illness in mental health-care because it could be useful to organize the spatial distribution of health-care services. Ordinal classification is used in this problem because the classes are: spatial unit with depression, spatial unit which could present depression and spatial unit where there is not depression. It is shown that the proposed method is capable of preserving the rank of data classes in a projected data space for this database. In comparison to other standard methods like C4.5, SVMRank, Adaboost, and MLP nominal classifiers, the proposed KDLOR algorithm is shown to be competitive.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121536767","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121671
A. Jamshidifar
In this paper, an Evolutionary approach for optimization of cyclic Gas Transmission Network (GTN) is presented. The GTNs comprise of nodes, links, compressor stations and valves where the last one is a main component of GTNs which generally not considered in similar works. In this approach, at first a reduced network will be generated from the original GTN and the cycles of the reduced network will be identified. Then an iterative approach will be used to find the cycles flows which optimize the objective function. This approach calculates the pressure variables at fixed flow rates using dynamic programming (DP) and updates the gas flow rates to improve the objective function in every iteration. The objective function is a weighted summation of total number of running compressor stations and their total fuel consumption. The flow rates will be updated using Genetic Algorithm (GA) which is modified to speed up its convergence. The main modifications are related to decomposing of chromosomes to subchromosomes and finding the upper and lower limits for crossover and mutation. A number of real examples of Iranian GTN are exploited to support the proposed approach.
{"title":"Optimization of natural gas transmission network using genetic algorithm","authors":"A. Jamshidifar","doi":"10.1109/ISDA.2011.6121671","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121671","url":null,"abstract":"In this paper, an Evolutionary approach for optimization of cyclic Gas Transmission Network (GTN) is presented. The GTNs comprise of nodes, links, compressor stations and valves where the last one is a main component of GTNs which generally not considered in similar works. In this approach, at first a reduced network will be generated from the original GTN and the cycles of the reduced network will be identified. Then an iterative approach will be used to find the cycles flows which optimize the objective function. This approach calculates the pressure variables at fixed flow rates using dynamic programming (DP) and updates the gas flow rates to improve the objective function in every iteration. The objective function is a weighted summation of total number of running compressor stations and their total fuel consumption. The flow rates will be updated using Genetic Algorithm (GA) which is modified to speed up its convergence. The main modifications are related to decomposing of chromosomes to subchromosomes and finding the upper and lower limits for crossover and mutation. A number of real examples of Iranian GTN are exploited to support the proposed approach.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114171403","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121751
E. Cables, M. García-Cascales, M. Gómez-López, M. T. Lamata
The rural venture farm needs to develop improved methods for evaluating the performance of its projects. We are interested in the problems of the implementation of a rabbit-breeding farm. One of the first decisions to be taken refers to the type of the structure for housing the animals. In general, greater environmental control requires more technology and a greater investment cost, but also yields higher levels of production which are, above all, uniform over time. Considering that we are faced with a problem that includes different, in some cases contradictory, aspects, we have decided to carry out a multicriteria analysis, using five evaluation criteria. We assume there is no quantitative information available for the decision but only linguistic information can be used. The main purpose of this paper is to study the problem by means of the fuzzy TOPSIS Method for multicriteria decision making, when the information regarding the alternatives is quantified by means of fuzzy numbers and the information about criteria is obtaining by means of a linear ordered weighted averaging operator.
{"title":"A framework for evaluating rabbit-breeding farm in the mediterranean: A TOPSIS approach","authors":"E. Cables, M. García-Cascales, M. Gómez-López, M. T. Lamata","doi":"10.1109/ISDA.2011.6121751","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121751","url":null,"abstract":"The rural venture farm needs to develop improved methods for evaluating the performance of its projects. We are interested in the problems of the implementation of a rabbit-breeding farm. One of the first decisions to be taken refers to the type of the structure for housing the animals. In general, greater environmental control requires more technology and a greater investment cost, but also yields higher levels of production which are, above all, uniform over time. Considering that we are faced with a problem that includes different, in some cases contradictory, aspects, we have decided to carry out a multicriteria analysis, using five evaluation criteria. We assume there is no quantitative information available for the decision but only linguistic information can be used. The main purpose of this paper is to study the problem by means of the fuzzy TOPSIS Method for multicriteria decision making, when the information regarding the alternatives is quantified by means of fuzzy numbers and the information about criteria is obtaining by means of a linear ordered weighted averaging operator.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120959703","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121762
G. M. Khan, Shahid N. Khan, F. Ullah
Load forecasting has been an inevitable issue in electric power supply in past. It is always desired to predict the load requirements in order to generate and supply electric power efficiently. In this research, a neuro-evolutionary technique known as Cartesian Genetic Algorithm evolved Artificial Neural Network (CGPANN) has been deployed to develop a peak load forecasting model for the prediction of peak loads 24 hours ahead. The proposed model presents the training of all the parameters of Artificial Neural Network (ANN) including: weights, topology and functionality of individual nodes. The network is trained both on annual as well as quarterly bases, thus obtaining a unique model for each season.
{"title":"Short-term daily peak load forecasting using fast learning neural network","authors":"G. M. Khan, Shahid N. Khan, F. Ullah","doi":"10.1109/ISDA.2011.6121762","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121762","url":null,"abstract":"Load forecasting has been an inevitable issue in electric power supply in past. It is always desired to predict the load requirements in order to generate and supply electric power efficiently. In this research, a neuro-evolutionary technique known as Cartesian Genetic Algorithm evolved Artificial Neural Network (CGPANN) has been deployed to develop a peak load forecasting model for the prediction of peak loads 24 hours ahead. The proposed model presents the training of all the parameters of Artificial Neural Network (ANN) including: weights, topology and functionality of individual nodes. The network is trained both on annual as well as quarterly bases, thus obtaining a unique model for each season.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120984194","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}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121818
M. Cruz-Ramírez, C. Hervás‐Martínez, J. Sánchez-Monedero, Pedro Antonio Gutiérrez
There are many metrics available to measure the goodness of a classifier when working with ordinal datasets. These measures are divided into product-moment and association metrics. In this paper, the behavior of several metrics is studied in different situations. In addition, two new measures associated with an ordinal classifier are defined: the maximum and the minimum mean absolute error of all the classes. From the results of this comparison, a pair of metrics is selected (one associated to the overall error and another one to the error of the class with lowest level of classification) to guide the evolution of a multi-objective evolutionary algorithm, obtaining good results in generalization on ordinal datasets.
{"title":"A preliminary study of ordinal metrics to guide a multi-objective evolutionary algorithm","authors":"M. Cruz-Ramírez, C. Hervás‐Martínez, J. Sánchez-Monedero, Pedro Antonio Gutiérrez","doi":"10.1109/ISDA.2011.6121818","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121818","url":null,"abstract":"There are many metrics available to measure the goodness of a classifier when working with ordinal datasets. These measures are divided into product-moment and association metrics. In this paper, the behavior of several metrics is studied in different situations. In addition, two new measures associated with an ordinal classifier are defined: the maximum and the minimum mean absolute error of all the classes. From the results of this comparison, a pair of metrics is selected (one associated to the overall error and another one to the error of the class with lowest level of classification) to guide the evolution of a multi-objective evolutionary algorithm, obtaining good results in generalization on ordinal datasets.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121722827","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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