2018 7th Brazilian Conference on Intelligent Systems (BRACIS)最新文献_第4页

Petroleum Reservoir Connectivity Patterns Reconstruction Using Deep Convolutional Generative Adversarial Networks 基于深度卷积生成对抗网络的油藏连通性模式重建

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00025

Rodrigo Exterkoetter, F. Bordignon, L. D. Figueiredo, M. Roisenberg, B. B. Rodrigues

引用次数: 1

Selecting Algorithms for the Quadratic Assignment Problem with a Multi-label Meta-Learning Approach 基于多标签元学习方法的二次分配问题算法选择

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00038

Augusto Lopez Dantas, Aurora Trinidad Ramirez Pozo

引用次数: 3

A Novel Equidistant-Scattering-Based Cluster Index 一种新的基于等距离分散的聚类索引

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00099

Caio Flexa, Reginaldo Santos, W. Gomes, C. Sales

引用次数: 0

Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements 转座要素层次分类中正反例的选择策略

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00079

Bruna Zamith Santos, G. Pereira, F. Nakano, R. Cerri

{"title":"Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements","authors":"Bruna Zamith Santos, G. Pereira, F. Nakano, R. Cerri","doi":"10.1109/BRACIS.2018.00079","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00079","url":null,"abstract":"Transposable Elements (TEs) are DNA sequences capable of changing the gene's activity through transposition within the cells of a host. Once TEs insert themselves in other genes, they can change or reduce the activity of certain proteins, which in some cases could unfeasible the survival of such organisms or even provide genetic variability. A variety of methods has been proposed for the identification and classification of TEs, but most of them still involve a lot of manual work or are too class-specific, which restricts its applicability. Besides, the classes involved in such problems are often hierarchically structured, which is ignored by most of these methods. In this scenario, one problem that still needs further investigation is the use of strategies for selecting positive and negative instances during the induction of hierarchical models. Therefore, in this paper we explore four distinct strategies for selecting training instances, making use of several Machine Learning classifiers with different biases which were applied to the Hierarchical Classification of TEs using a local approach. Thus, we recommend the best strategy based on the results experimentally obtained.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129145401","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

Influence of Reference Points on a Many-Objective Optimization Algorithm 参考点对多目标优化算法的影响

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00014

Matheus Carvalho, André Britto

{"title":"Influence of Reference Points on a Many-Objective Optimization Algorithm","authors":"Matheus Carvalho, André Britto","doi":"10.1109/BRACIS.2018.00014","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00014","url":null,"abstract":"Many-Objective Optimization Problems (MaOPs) are problems that have more than three objective functions to be optimized. Most Multi-Objective Evolutionary Algorithms scales poorly when the number of objective function increases. To face this limitation, new strategies have been proposed. One of them is the use of reference points to enhance the search of the algorithms. NSGA-III is a reference point based algorithm that has been successfully applied to solve MaOPs. NSGA-III uses a set of reference points placed on a normalized hyperplane which is equally inclined to all objective axes and has an intercept at 1 on each axis. Despite the good results of NSGA-III, the shape of the hyper-plane is not deeply explored in literature. This work studies the influence of the set of reference pointsonMany-ObjectiveOptimization.Here, itisproposedthree new transformations of the reference points set used by NSGAIII. Besides, the Vector Guided Adaptation procedure is also applied to modify original NSGA-III hyper-plane. Furthermore, an adaptation of NSGA-III algorithm is proposed and it is performed a set of experiments to evaluate the transformation procedures. Original and adapted versions of NSGA-III are faced over several benchmarking problems observing both convergence and diversity through the analysis of statistical tests.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129851364","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: 2

Exploring Evolutive Methods for Cloud Provider Selection Based on Performance Indicators 基于绩效指标的云提供商选择的进化方法探索

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00035

Lucas Borges de Moraes, Adriano Fiorese, R. S. Parpinelli

引用次数: 1

Solving a Spatial Puzzle Using Answer Set Programming Integrated with Markov Decision Process 结合马尔可夫决策过程的答案集规划求解空间谜题

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00097

Thiago Freitas dos Santos, P. Santos, L. Ferreira, Reinaldo A. C. Bianchi, Pedro Cabalar

引用次数: 1

Process Mining Discovery Techniques in a Low-Structured Process Works? 低结构过程工程中的过程挖掘发现技术?

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00042

Raphael J. D'Castro, Adriano Oliveira, Augusto H. Terra

引用次数: 1

Instance Selection and Class Balancing Techniques for Cross Project Defect Prediction 跨项目缺陷预测的实例选择和类平衡技术

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00101

Alysson Bispo, R. Prudêncio, D. V. D. Silva

{"title":"Instance Selection and Class Balancing Techniques for Cross Project Defect Prediction","authors":"Alysson Bispo, R. Prudêncio, D. V. D. Silva","doi":"10.1109/BRACIS.2018.00101","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00101","url":null,"abstract":"Various software metrics and statistical models have been developed to help companies to predict software defects. Traditional software defect prediction approaches use historical data about previous bugs on a project in order to build predictive machine learning models. However, in many cases the historical testing data available in a project is scarce, i.e., very few or even no labeled training instances are available, which will result on a low quality defect prediction model. In order to overcome this limitation, Cross-Project Defect Prediction (CPDP) can be adopted to learn a defect prediction model for a project of interest (i.e., a target project) by reusing (transferring) data collected from several previous projects (i.e., source projects). In this paper, we focused on neighborhood-based instance selection techniques for CPDP which select labeled instances in the source projects that are similar to the unlabeled instances available in the target project. Despite its simplicity, these techniques have limitations which were addressed in our work. First, although they can select representative source instances, the quality of the selected instances is usually not addressed. Additionally, bug prediction datasets are normally unbalanced (i.e., there are more nondefect instances than defect ones), which can harm learning performance. In this paper, we proposed a new transfer learning approach for CPDP, in which instances selected by a neighborhood-based technique are filtered by the FuzzyRough Instance Selection (FRIS) technique in order to remove noisy instances in the training set. Following, in order to solve class balancing problems, the Synthetic Minority Oversampling Technique (SMOTE) technique is adopted to oversample the minority (defect-prone) class, thus increasing the chance of finding bugs correctly. Experiments were performed on a benchmark set of Java projects, achieving promising results.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704751","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

Bio-Inspired and Heuristic Methods Applied to a Benchmark of the Task Scheduling Problem 生物启发和启发式方法在任务调度问题基准中的应用

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00095

T. I. D. Carvalho, Bruno Well Dantas Morais, G. Oliveira

{"title":"Bio-Inspired and Heuristic Methods Applied to a Benchmark of the Task Scheduling Problem","authors":"T. I. D. Carvalho, Bruno Well Dantas Morais, G. Oliveira","doi":"10.1109/BRACIS.2018.00095","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00095","url":null,"abstract":"Task scheduling seeks for the time-efficient allocation of the tasks of a parallel program to a multiprocessor system. Being intractable, heuristic methods have been developed to solve this problem. Among the more traditional approaches, approximate techniques as constructive list-based heuristics or simply random schedulers have been extensively employed. On the other hand, bio-inspired models, such as cellular automata (CA) and evolutionary-based schedulers, have been recently investigated as alternative approaches. However, the comparative analysis of the experimental results is primarily limited by the capacity of benchmarks to represent the problem in a full range of difficulty. Aiming to investigate the usage of a more comprehensive benchmark on comparative experiments, we have developed a set of scheduling instances based on real-world programs by applying variations in their features, including number of tasks, number of available processors and communication costs. We have applied two simple heuristics to serve both as baselines for performance and to evaluate the complexity of each problem instance as basis for comparison. Moreover, we investigate here three bio-inspired schedulers applied to the same instances. Two of them are genetic algorithm (GA) approaches while the third employs a GA to find good CA rules able to schedule unseen instances of a parallel program in a very fast operation. Our results show that the CA-based scheduler outperforms the other methods significantly on mosts instances while, on certain instances of the problem, a good solution can be produced consistently by a heuristic based on random allocations. We conclude that these instances are unfit for benchmark purposes and that there is a necessity of careful analysis and selection of problem instances for performance evaluation in this field of research.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114632256","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