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2016 5th Brazilian Conference on Intelligent Systems (BRACIS)最新文献

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On Validation Setup for Multiclass Imbalanced Data Sets 多类不平衡数据集的验证设置
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.090
Evandro J. R. Silva, C. Zanchettin
{"title":"On Validation Setup for Multiclass Imbalanced Data Sets","authors":"Evandro J. R. Silva, C. Zanchettin","doi":"10.1109/BRACIS.2016.090","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.090","url":null,"abstract":"The validation of experiments is commonly evaluated with Cross-Validation methods. In the literature the 10-fold, followed by bootstrap, are the most indicated methods. However there lacks a study of a proper validation procedure for imbalanced data sets, specially for the rare class case. In this work the most used validation methods were tested in ten imbalanced data sets, with a generic and an ad hoc classifiers. Analyses showed that 10-fold, followed by hold-out, are the indicated methods when using a generic classifier. For the ad hoc classifier the 10-fold, followed by bootstrap, are the indicated ones. In the case of rare classes in a data set, the most indicated method is the repeated hold-out.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"2 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120809799","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}
引用次数: 0
Multi-class Emotions Classification by Sentic Levels as Features in Sentiment Analysis 情感分析中以情感层次为特征的多类情感分类
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.093
A. M. G. Almeida, Sylvio Barbon Junior, E. Paraiso
{"title":"Multi-class Emotions Classification by Sentic Levels as Features in Sentiment Analysis","authors":"A. M. G. Almeida, Sylvio Barbon Junior, E. Paraiso","doi":"10.1109/BRACIS.2016.093","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.093","url":null,"abstract":"Sentiment Analysis has become a critical research area in recent days and pervasive in real life. Considering the identification of Emotions from textual content, we propose the Hourglass of Emotions as the feature that comes from the intensity of affective dimensions and combination thereof. Thus, based on a news dataset labeled with six primary Emotions, we intend to solve the Multi-class Classification Problem comparing decomposition methods - One against All and One Against One - and several aggregation methods. As base classifiers algorithms, we adopted Support Vector Machine, Naive Bayes, Decision Tree and Random Forests. Anchored on the results, we found that it is feasible to use this new set of features. The combination of Support Vector Machine and WENG pairwise coupling method was the best one, producing an accuracy of 55.91%.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129526298","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}
引用次数: 4
Determining the Structure of Decision Directed Acyclic Graphs for Multiclass Classification Problems 多类分类问题中决策有向无环图结构的确定
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.031
Thaise M. Quiterio, Ana Carolina Lorena
{"title":"Determining the Structure of Decision Directed Acyclic Graphs for Multiclass Classification Problems","authors":"Thaise M. Quiterio, Ana Carolina Lorena","doi":"10.1109/BRACIS.2016.031","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.031","url":null,"abstract":"An usual strategy to solve multiclass classification problems in Machine Learning is to decompose them into multiple binary sub-problems. The final multiclass prediction is obtained by a proper combination of the outputs of the binary classifiers induced in their solution. Decision directed acyclic graphs (DDAG) can be used to organize and to aggregate the outputs of the pairwise classifiers from the one-versus-one (OVO) decomposition. Nonetheless, there are various possible DDAG structures for problems with many classes. In this paper evolutionary algorithms are employed to heuristically find the positions of the OVO binary classifiers in a DDAG. The objective is to place easier sub-problems at higher levels of the DDAG hierarchical structure, in order to minimize the occurrence of cumulative errors. For estimating the complexity of the binary sub-problems, we employ two indexes which measure the separability of the classes. The proposed approach presented sound results in a set of experiments on benchmark datasets, although random DDAGs also performed quite well.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126369394","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}
引用次数: 6
Novelty Detection Based on Genuine Normal and Artificially Generated Novelty Examples 基于真实常态和人工生成新颖性样本的新颖性检测
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.065
George G. Cabral, Adriano Oliveira
{"title":"Novelty Detection Based on Genuine Normal and Artificially Generated Novelty Examples","authors":"George G. Cabral, Adriano Oliveira","doi":"10.1109/BRACIS.2016.065","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.065","url":null,"abstract":"One-class classification (OCC) is an important problem with applications in several different areas such as outlier detection and machine monitoring. Since in OCC there are no examples of the novelty class, the description generated may be a tight or a bulky description. Both cases are undesirable. In order to create a proper description, the presence of examples of the novelty class is very important. However, such examples may be rare or absent during the modeling phase. In these cases, the artificial generation of novelty samples may overcome this limitation. In this work it is proposed a two steps approach for generating artificial novelty examples in order to guide the parameter optimization process. The results show that the adopted approach has shown to be competitive with the results achieved when using real (genuine) novelty samples.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124246802","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}
引用次数: 0
No-Reference Image Quality Assessment Using Texture Information Banks 使用纹理信息库的无参考图像质量评估
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.033
P. Freitas, W. Y. L. Akamine, Mylène C. Q. Farias
{"title":"No-Reference Image Quality Assessment Using Texture Information Banks","authors":"P. Freitas, W. Y. L. Akamine, Mylène C. Q. Farias","doi":"10.1109/BRACIS.2016.033","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.033","url":null,"abstract":"In this paper, we propose a new no-reference quality assessment method which uses a machine learning technique based on texture analysis. The proposed method compares test images with texture images of a public database. Local Binary Patterns (LBPs) are used as local texture feature descriptors. With a Csiszár-Morimoto divergence measure, the histograms of the LBPs of the test images are compared with the histograms of the LBPs of the database texture images, generating a set of difference measures. These difference measures are used to blindly predict the quality of an image. Experimental results show that the proposed method is fast and has a good quality prediction power, outperforming other no-reference image quality assessment methods.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127903535","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}
引用次数: 3
Transforming Multi-agent Planning Into Single-Agent Planning Using Best-Cost Strategy 利用最优成本策略将多智能体规划转化为单智能体规划
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.089
L. Moreira, C. Ralha
{"title":"Transforming Multi-agent Planning Into Single-Agent Planning Using Best-Cost Strategy","authors":"L. Moreira, C. Ralha","doi":"10.1109/BRACIS.2016.089","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.089","url":null,"abstract":"This article presents a method to transform a multi-agent planning (MAP) problem into single-agent tasks through a goal delegation process using a best-cost strategy. This research work combines different properties of planning methods that describe the state of the art of MAP. The planning approach takes into consideration the cost associated to the action and the action sequence related to the set of goals that each agent must achieve. The proposed method was validated using the Graphplan planner taking into consideration the optimal solution in relation to cost, time and use of memory during the planning process. The results show the potential of the approach that can face the exponential growth-rate of planning time through the delegation of goals using a best-cost strategy to transform a MAP problem into as many as necessary single-agent tasks.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128339681","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}
引用次数: 3
Investigating Selection Strategies in Multi-objective Probabilistic Model Based Algorithms 基于多目标概率模型算法的选择策略研究
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.013
A. Strickler, Olacir Rodrigues Castro Junior, A. Pozo, Roberto Santana
{"title":"Investigating Selection Strategies in Multi-objective Probabilistic Model Based Algorithms","authors":"A. Strickler, Olacir Rodrigues Castro Junior, A. Pozo, Roberto Santana","doi":"10.1109/BRACIS.2016.013","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.013","url":null,"abstract":"Recent advances on multi-objective evolutionary algorithm (MOEAs) have acknowledged the important role played by selection, replacement, and archiving strategies in the behavior of these algorithms. However, the influence of these methods has been scarcely investigated for the particular class of MOEAs that use probabilistic modeling of the solutions. In this paper we fill this void by proposing an analysis of the role of the aforementioned strategies on an extensive set of bi-objective functions. We focus on the class of algorithms that use Gaussian univariate marginal models, and study how typical selection and replacement strategies used together with this probabilistic model impact the behavior of the search. Our analysis is particularized for a set of bi-objective functions that exhibit a representative set of characteristics (e.g. decomposable, ill-conditioned, non-linear, etc.). The experimental results shows that MOEAs that use simple probabilistic modeling outperform traditional MOEAs based on crossover operators.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133073369","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}
引用次数: 1
Automatic Selection of Learning Bias for Active Sampling 主动抽样中学习偏差的自动选择
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.021
Davi Pereira dos Santos, A. Carvalho
{"title":"Automatic Selection of Learning Bias for Active Sampling","authors":"Davi Pereira dos Santos, A. Carvalho","doi":"10.1109/BRACIS.2016.021","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.021","url":null,"abstract":"The classification task, when performed by machine learning algorithms, requires previous training on labeled instances. In many applications, the data labeling process is expensive and can affect the predictive performance of classification models. A current solution has been the use of active learning, which investigates strategies for data labeling. Its main goal is to decide which instances should be labeled and added to the training set, reducing the overall labeling costs. However, the strategy normally depends on a learning algorithm, which should be chosen by a machine learning specialist - usually based on a cross-validation procedure. Consequently, there is a deadlock: without the complete training set, the algorithm that will present the best learning curve cannot be known in advance. Ideally, some type of automatic selection should be employed to solve this deadlock. This study investigates the use of meta-learning for automatic algorithm selection in active learning tasks. Experimental results show that meta-learning is able to find correspondences between algorithms and dataset features in order to help active learning to reduce the risks of incurring in unexpected labeling costs.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134631472","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}
引用次数: 1
Tree-Based Grammar Genetic Programming to Evolve Particle Swarm Algorithms 基于树的语法遗传规划进化粒子群算法
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.016
P. Miranda, R. Prudêncio
{"title":"Tree-Based Grammar Genetic Programming to Evolve Particle Swarm Algorithms","authors":"P. Miranda, R. Prudêncio","doi":"10.1109/BRACIS.2016.016","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.016","url":null,"abstract":"Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-guided Genetic Programming algorithms (GGGP), in special, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. In this work, we proposed a tree-based GGGP technique for the generation of PSO algorithms. This paper intends to investigate whether this approach can improve the production of PSO algorithms when compared to other GGGP techniques already used to solve the current problem. In the experiments, a comparison between the tree-based and the commonly used linearized GGGP approach for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-art optimization algorithms, and the results showed that the algorithms produced by the tree-based GGGP achieved competitive results.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808543","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}
引用次数: 6
Automated Left Ventricle Posterior Wall Segmentation Using Kohonen Self-Organizing Map 基于Kohonen自组织图的左心室后壁自动分割
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.088
Salety Ferreira Baracho, V. V. D. Melo, R. C. Coelho
{"title":"Automated Left Ventricle Posterior Wall Segmentation Using Kohonen Self-Organizing Map","authors":"Salety Ferreira Baracho, V. V. D. Melo, R. C. Coelho","doi":"10.1109/BRACIS.2016.088","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.088","url":null,"abstract":"Image segmentation of left ventricle using long-axis view of the echocardiogram is important to assist the operator in the extraction of functional parameters. The correct obtaining of this parameter can help an early diagnosis of such disease and is welcome to the medical community. However, it is not such an easy task due to the inherent equipment operator bias and the inter-and intra-observer variability. To aid in such issue, in this paper we present an automatic segmentation of the left ventricle posterior wall in echocardiographic images. Our approach employs the Self-Organizing Map to cluster the image's pixels and some image processing methods to perform the final segmentation and calculation of the left ventricle thickness. Results show that our approach, besides fully automatic, is more accurate than similar result from the literature obtained with semi-automatic methods.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"758 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133963237","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}
引用次数: 3
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