Pub Date : 2013-05-20DOI: 10.4236/JILSA.2013.52012
M. K. Dalal, M. Zaveri
Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming; statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models—the na?ve Bayesian model and the artificial neural network model. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the na?ve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.
{"title":"Automatic Classification of Unstructured Blog Text","authors":"M. K. Dalal, M. Zaveri","doi":"10.4236/JILSA.2013.52012","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52012","url":null,"abstract":"Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming; statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models—the na?ve Bayesian model and the artificial neural network model. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the na?ve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"108-114"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329629","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 : 2013-05-20DOI: 10.4236/JILSA.2013.52010
Reza Khorram-Nia, Aliasghar Baziar, A. Kavousi-fard
This paper proposes a new stochastic framework based on the probabilistic load flow to consider the uncertainty effects in the Distribution Static Compensator (DSTATCOM) allocation and sizing problem. The proposed method is based on the point estimate method (PEM) to capture the uncertainty associated with the forecast error of the loads. In order to explore the search space globally, a new optimization algorithm based on bat algorithm (BA) is proposed too. The objective functions to be investigated are minimization of the total active power losses and reducing the voltage deviation of the buses. Also to reach a proper balance between the optimization of both the objective functions, the idea of interactive fuzzy satisfying method is employed in the multi-objective formulation. The feasibility and satisfying performance of the proposed method is examined on the 69-bus IEEE distribution system.
{"title":"A Novel Stochastic Framework for the Optimal Placement and Sizing of Distribution Static Compensator","authors":"Reza Khorram-Nia, Aliasghar Baziar, A. Kavousi-fard","doi":"10.4236/JILSA.2013.52010","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52010","url":null,"abstract":"This paper proposes a new stochastic framework based on the probabilistic load flow to consider the uncertainty effects in the Distribution Static Compensator (DSTATCOM) allocation and sizing problem. The proposed method is based on the point estimate method (PEM) to capture the uncertainty associated with the forecast error of the loads. In order to explore the search space globally, a new optimization algorithm based on bat algorithm (BA) is proposed too. The objective functions to be investigated are minimization of the total active power losses and reducing the voltage deviation of the buses. Also to reach a proper balance between the optimization of both the objective functions, the idea of interactive fuzzy satisfying method is employed in the multi-objective formulation. The feasibility and satisfying performance of the proposed method is examined on the 69-bus IEEE distribution system.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"90-98"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4236/JILSA.2013.52010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329568","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 : 2013-05-20DOI: 10.4236/JILSA.2013.52014
M. Atsumi
This paper proposes a probabilistic model of object category learning in conjunction with attention-guided organized perception. This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on dynamically-formed Markov random fields around salient preattentive points and co-occurring segments are grouped in the neighborhood of selective attended segments. In object category learning, a set of classes of each object category is obtained based on the probabilistic latent component analysis with the variable number of classes from bags of features of segments extracted from images which contain the categorical objects in context and an object category is represented by a composite of object classes. Through experiments using two image data sets, it is shown that the model learns a probabilistic structure of intra-categorical composition and inter-categorical difference of object categories and achieves high performance in object category recognition.
{"title":"Attention-Guided Organized Perception and Learning of Object Categories Based on Probabilistic Latent Variable Models","authors":"M. Atsumi","doi":"10.4236/JILSA.2013.52014","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52014","url":null,"abstract":"This paper proposes a probabilistic model of object category learning in conjunction with attention-guided organized perception. This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on dynamically-formed Markov random fields around salient preattentive points and co-occurring segments are grouped in the neighborhood of selective attended segments. In object category learning, a set of classes of each object category is obtained based on the probabilistic latent component analysis with the variable number of classes from bags of features of segments extracted from images which contain the categorical objects in context and an object category is represented by a composite of object classes. Through experiments using two image data sets, it is shown that the model learns a probabilistic structure of intra-categorical composition and inter-categorical difference of object categories and achieves high performance in object category recognition.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"2013 1","pages":"123-133"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329680","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 : 2013-05-20DOI: 10.4236/JILSA.2013.52009
Mayy M. Al-Tahrawi
In this paper, the role of rare or infrequent terms in enhancing the accuracy of English Text Categorization using Polynomial Networks (PNs) is investigated. To study the impact of rare terms in enhancing the accuracy of PNs-based text categorization, different term reduction criteria as well as different term weighting schemes were experimented on the Reuters Corpus using PNs. Each term weighting scheme on each reduced term set was tested once keeping the rare terms and another time removing them. All the experiments conducted in this research show that keeping rare terms substantially improves the performance of Polynomial Networks in Text Categorization, regardless of the term reduction method, the number of terms used in classification, or the term weighting scheme adopted.
{"title":"The Role of Rare Terms in Enhancing the Performance of Polynomial Networks Based Text Categorization","authors":"Mayy M. Al-Tahrawi","doi":"10.4236/JILSA.2013.52009","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52009","url":null,"abstract":"In this paper, the role of rare or infrequent terms in enhancing the accuracy of English Text Categorization using Polynomial Networks (PNs) is investigated. To study the impact of rare terms in enhancing the accuracy of PNs-based text categorization, different term reduction criteria as well as different term weighting schemes were experimented on the Reuters Corpus using PNs. Each term weighting scheme on each reduced term set was tested once keeping the rare terms and another time removing them. All the experiments conducted in this research show that keeping rare terms substantially improves the performance of Polynomial Networks in Text Categorization, regardless of the term reduction method, the number of terms used in classification, or the term weighting scheme adopted.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"84-89"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329535","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 : 2013-02-22DOI: 10.4236/JILSA.2013.51006
Rongshuai Li, A. Mita, Jin Zhou
This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.
{"title":"Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis","authors":"Rongshuai Li, A. Mita, Jin Zhou","doi":"10.4236/JILSA.2013.51006","DOIUrl":"https://doi.org/10.4236/JILSA.2013.51006","url":null,"abstract":"This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"48-56"},"PeriodicalIF":0.0,"publicationDate":"2013-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329935","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 : 2013-02-22DOI: 10.4236/JILSA.2013.51005
C. Neelamegam, Vishnuvardhan Sapineni, V. Muthukumaran, Jayakumar Tamanna
Reduced-activated ferritic-martensitic steels are being considered for use in fusion energy reactor and subsequent fusion power reactor applications. Typically, those reduced activated steels can loose their radioactivity in approximately 100 years, compared to thousands of years for the non-reduced-activated steels. The commonly used welding process for fabricating this steel are electron-beam welding, and tungsten inert gas (TIG) welding. Therefore, Activated-flux tungsten inert gas (A-TIG) welding, a variant of TIG welding has been developed in-house to increase the depth of penetration in single pass welding. In structural materials produced by A-TIG welding process, weld bead width, depth of penetration and heat affected zone (HAZ) width play an important role in determining in mechanical properties and also the performance of the weld joints during service. To obtain the desired weld bead geometry, HAZ width and make a good weld joint, it becomes important to set up the welding process parameters. The current work attempts to develop independent models correlating the welding process parameters like current, voltage and torch speed with weld bead shape will bead shape parameters like depth of penetration, bead width, HAZ width using ANFIS. These models will be used to evaluate the objective function in the genetic algorithm. Then genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.
{"title":"Hybrid Intelligent Modeling for Optimizing Welding Process Parameters for Reduced Activation Ferritic-Martensitic (RAFM) Steel","authors":"C. Neelamegam, Vishnuvardhan Sapineni, V. Muthukumaran, Jayakumar Tamanna","doi":"10.4236/JILSA.2013.51005","DOIUrl":"https://doi.org/10.4236/JILSA.2013.51005","url":null,"abstract":"Reduced-activated ferritic-martensitic steels are being considered for use in fusion energy reactor and subsequent fusion power reactor applications. Typically, those reduced activated steels can loose their radioactivity in approximately 100 years, compared to thousands of years for the non-reduced-activated steels. The commonly used welding process for fabricating this steel are electron-beam welding, and tungsten inert gas (TIG) welding. Therefore, Activated-flux tungsten inert gas (A-TIG) welding, a variant of TIG welding has been developed in-house to increase the depth of penetration in single pass welding. In structural materials produced by A-TIG welding process, weld bead width, depth of penetration and heat affected zone (HAZ) width play an important role in determining in mechanical properties and also the performance of the weld joints during service. To obtain the desired weld bead geometry, HAZ width and make a good weld joint, it becomes important to set up the welding process parameters. The current work attempts to develop independent models correlating the welding process parameters like current, voltage and torch speed with weld bead shape will bead shape parameters like depth of penetration, bead width, HAZ width using ANFIS. These models will be used to evaluate the objective function in the genetic algorithm. Then genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"39-47"},"PeriodicalIF":0.0,"publicationDate":"2013-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329919","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 : 2013-02-22DOI: 10.4236/JILSA.2013.51002
Aliasghar Baziar, Abdollah Kavoosi-Fard, J. Zare
In the new competitive electricity market, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach to supply the electrical consumers more reliably and economically. In this regard, this paper proposes a novel solution methodology based on bat algorithm to solve the op- timal energy management of MG including several RESs with the back-up of Fuel Cell (FC), Wind Turbine (WT), Photovoltaics (PV), Micro Turbine (MT) as well as storage devices to meet the energy mismatch. The problem is formulated as a nonlinear constraint optimization problem to minimize the total cost of the grid and RESs, simultaneously. In addition, the problem considers the interactive effects of MG and utility in a 24 hour time interval which would in- crease the complexity of the problem from the optimization point of view more severely. The proposed optimization technique is consisted of a self adaptive modification method compromised of two modification methods based on bat algorithm to explore the total search space globally. The superiority of the proposed method over the other well-known algorithms is demonstrated through a typical renewable MG as the test system.
{"title":"A Novel Self Adaptive Modification Approach Based on Bat Algorithm for Optimal Management of Renewable MG","authors":"Aliasghar Baziar, Abdollah Kavoosi-Fard, J. Zare","doi":"10.4236/JILSA.2013.51002","DOIUrl":"https://doi.org/10.4236/JILSA.2013.51002","url":null,"abstract":"In the new competitive electricity market, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach to supply the electrical consumers more reliably and economically. In this regard, this paper proposes a novel solution methodology based on bat algorithm to solve the op- timal energy management of MG including several RESs with the back-up of Fuel Cell (FC), Wind Turbine (WT), Photovoltaics (PV), Micro Turbine (MT) as well as storage devices to meet the energy mismatch. The problem is formulated as a nonlinear constraint optimization problem to minimize the total cost of the grid and RESs, simultaneously. In addition, the problem considers the interactive effects of MG and utility in a 24 hour time interval which would in- crease the complexity of the problem from the optimization point of view more severely. The proposed optimization technique is consisted of a self adaptive modification method compromised of two modification methods based on bat algorithm to explore the total search space globally. The superiority of the proposed method over the other well-known algorithms is demonstrated through a typical renewable MG as the test system.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"11-18"},"PeriodicalIF":0.0,"publicationDate":"2013-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4236/JILSA.2013.51002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329714","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 : 2013-02-22DOI: 10.4236/JILSA.2013.51007
G. C. Pereira, Marilia Mitidieri F. Oliveira, N. Ebecken
Since viruses are able to influence the trophic status and community structure they should be accessed and accounted in ecosystem functioning and management models. So, this work met a set of biological, chemical and physical time series in order to explore the correlations with marine virioplankton community across different trophic gradients. The case studied is the Arraial do Cabo upwelling system, northeast of Rio de Janeiro State in Southeast coast of Brazil. The main goal is to evolve three type of artificial neural network (ANN) by genetic algorithm (GA) optimization to predict virioplankton abundance and dynamic. The input variables range from the abundance of phytoplankton, bacterioplankton and its ratios acquired by one in situ and another ex situ flow cytometers. These data were collected with weekly frequency from August 2006 to June 2007. Our results show viruses being highly correlated to their host, and that GA provided an efficient method of optimizing ANN architectures to predict the virioplankton abundance. The RBF-NN model presented the best performance to an accuracy of 97% for any period in the year. A discussion and ecological interpretations about the system behavior is also provided.
由于病毒能够影响营养状况和群落结构，它们应该在生态系统功能和管理模型中被获取和考虑。因此，本研究通过一系列的生物、化学和物理时间序列来探索不同营养梯度与海洋浮游生物群落的相关性。研究的案例是巴西东南海岸巴西里约热内卢州东北部的Arraial do Cabo上升流系统。主要目的是通过遗传算法(GA)优化进化出三种类型的人工神经网络(ANN)来预测浮游生物的丰度和动态。输入变量的范围从浮游植物、浮游细菌的丰度及其由一个原位和另一个非原位流式细胞仪获得的比率。这些数据于2006年8月至2007年6月以每周频率收集。我们的研究结果表明，病毒与其宿主高度相关，遗传算法提供了一种有效的方法来优化人工神经网络架构，以预测浮游生物的丰度。RBF-NN模型在一年中任何时期的准确率都达到97%，表现最佳。对系统行为进行了讨论和生态解释。
{"title":"Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data","authors":"G. C. Pereira, Marilia Mitidieri F. Oliveira, N. Ebecken","doi":"10.4236/JILSA.2013.51007","DOIUrl":"https://doi.org/10.4236/JILSA.2013.51007","url":null,"abstract":"Since viruses are able to influence the trophic status and community structure they should be accessed and accounted in ecosystem functioning and management models. So, this work met a set of biological, chemical and physical time series in order to explore the correlations with marine virioplankton community across different trophic gradients. The case studied is the Arraial do Cabo upwelling system, northeast of Rio de Janeiro State in Southeast coast of Brazil. The main goal is to evolve three type of artificial neural network (ANN) by genetic algorithm (GA) optimization to predict virioplankton abundance and dynamic. The input variables range from the abundance of phytoplankton, bacterioplankton and its ratios acquired by one in situ and another ex situ flow cytometers. These data were collected with weekly frequency from August 2006 to June 2007. Our results show viruses being highly correlated to their host, and that GA provided an efficient method of optimizing ANN architectures to predict the virioplankton abundance. The RBF-NN model presented the best performance to an accuracy of 97% for any period in the year. A discussion and ecological interpretations about the system behavior is also provided.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"2013 1","pages":"57-66"},"PeriodicalIF":0.0,"publicationDate":"2013-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329976","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 : 2013-02-22DOI: 10.4236/JILSA.2013.51004
M. Buscema, Marco Breda, W. Lodwick
This article shows the efficacy of TWIST, a methodology for the design of training and testing data subsets extracted from given dataset associated with a problem to be solved via ANNs. The methodology we present is embedded in algorithms and actualized in computer software. Our methodology as implemented in software is compared to the current standard methods of random cross validation: 10-Fold CV, random split into two subsets and the more advanced T&T. For each strategy, 13 learning machines, representing different families of the main algorithms, have been trained and tested. All algorithms were implemented using the well-known WEKA software package. On one hand a falsification test with randomly distributed dependent variable has been used to show how T&T and TWIST behaves as the other two strategies: when there is no information available on the datasets they are equivalent. On the other hand, using the real Statlog (Heart) dataset, a strong difference in accuracy is experimentally proved. Our results show that TWIST is superior to current methods. Pairs of subsets with similar probability density functions are generated, without coding noise, according to an optimal strategy that extracts the most useful information for pattern classification.
{"title":"Training with Input Selection and Testing (TWIST) Algorithm: A Significant Advance in Pattern Recognition Performance of Machine Learning","authors":"M. Buscema, Marco Breda, W. Lodwick","doi":"10.4236/JILSA.2013.51004","DOIUrl":"https://doi.org/10.4236/JILSA.2013.51004","url":null,"abstract":"This article shows the efficacy of TWIST, a methodology for the design of training and testing data subsets extracted from given dataset associated with a problem to be solved via ANNs. The methodology we present is embedded in algorithms and actualized in computer software. Our methodology as implemented in software is compared to the current standard methods of random cross validation: 10-Fold CV, random split into two subsets and the more advanced T&T. For each strategy, 13 learning machines, representing different families of the main algorithms, have been trained and tested. All algorithms were implemented using the well-known WEKA software package. On one hand a falsification test with randomly distributed dependent variable has been used to show how T&T and TWIST behaves as the other two strategies: when there is no information available on the datasets they are equivalent. On the other hand, using the real Statlog (Heart) dataset, a strong difference in accuracy is experimentally proved. Our results show that TWIST is superior to current methods. Pairs of subsets with similar probability density functions are generated, without coding noise, according to an optimal strategy that extracts the most useful information for pattern classification.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"29-38"},"PeriodicalIF":0.0,"publicationDate":"2013-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329853","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 : 2013-02-22DOI: 10.4236/JILSA.2013.51003
P. Rivas-Perea, Juan Cota-Ruiz, J. Venzor, D. G. Chaparro, J. Rosiles
In this paper we study the problem of model selection for a linear programming-based support vector machine for regression. We propose generalized method that is based on a quasi-Newton method that uses a globalization strategy and an inexact computation of first order information. We explore the case of two-class, multi-class, and regression problems. Simulation results among standard datasets suggest that the algorithm achieves insignificant variability when measuring residual statistical properties.
{"title":"LP-SVR Model Selection Using an Inexact Globalized Quasi-Newton Strategy","authors":"P. Rivas-Perea, Juan Cota-Ruiz, J. Venzor, D. G. Chaparro, J. Rosiles","doi":"10.4236/JILSA.2013.51003","DOIUrl":"https://doi.org/10.4236/JILSA.2013.51003","url":null,"abstract":"In this paper we study the problem of model selection for a linear programming-based support vector machine for regression. We propose generalized method that is based on a quasi-Newton method that uses a globalization strategy and an inexact computation of first order information. We explore the case of two-class, multi-class, and regression problems. Simulation results among standard datasets suggest that the algorithm achieves insignificant variability when measuring residual statistical properties.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"1 1","pages":"19-28"},"PeriodicalIF":0.0,"publicationDate":"2013-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329757","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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