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

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A Comparative Study of Group Profiling Techniques in Co-authorship Networks 合作作者网络中群体分析技术的比较研究
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.074
J. Gomes, R. Prudêncio, André C. A. Nascimento
{"title":"A Comparative Study of Group Profiling Techniques in Co-authorship Networks","authors":"J. Gomes, R. Prudêncio, André C. A. Nascimento","doi":"10.1109/BRACIS.2016.074","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.074","url":null,"abstract":"Group profiling methods aim to construct a descriptive profile for communities in complex networks. The application of such methods in the analysis of co-authorship networks enables us to move forward in understanding the scientific communities, leading to new approaches to strengthen and expand scientific collaboration networks. This task is similar to the document cluster labeling task, which encourages the adaptation of cluster labeling methods for group profiling problems. In this work, we present a comparative study of group profiling and cluster labeling algorithms in a co-authorship network. A qualitative survey was conducted to evaluate the generated profiles, as well as the pros and cons of different profiling strategies, were analyzed with concrete examples. The results demonstrated a similar performance of both group profiling and cluster labeling methods.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"29 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":"133353645","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
An Unsupervised Particle Swarm Optimization Approach for Opinion Clustering 意见聚类的无监督粒子群优化方法
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.063
E. Souza, Adriano Oliveira, Gustavo H. F. M. Oliveira, Alisson Silva, Diego Santos
{"title":"An Unsupervised Particle Swarm Optimization Approach for Opinion Clustering","authors":"E. Souza, Adriano Oliveira, Gustavo H. F. M. Oliveira, Alisson Silva, Diego Santos","doi":"10.1109/BRACIS.2016.063","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.063","url":null,"abstract":"Supervised machine learning (ML) and lexicon-based are the most frequent approaches for opinion mining (OM), but they require considerable effort for preparing the training data and to build the opinion lexicon, respectively. This paper presents two unsupervised approaches for OM based on Particle Swarm Optimization (PSO). The PSO-based approaches were evaluated by eighteen experiments with different corpora types, domains, language, class balancing and pre-processing techniques. The proposed approaches achieved better accuracy on twelve experiments. Best results were obtained on corpora with a reduced number of dimensions and for specific domains. Best accuracy (0.79) was obtained by Discrete IDPSO on the OBCC corpus, outperforming supervised ML and lexicon-based approaches for this corpus.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"9 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":"133755641","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: 7
Musical Scales Recognition via Deterministic Walk in a Graph 基于确定性图行走的音阶识别
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.037
Andres Eduardo Coca Salazar, Liang Zhao
{"title":"Musical Scales Recognition via Deterministic Walk in a Graph","authors":"Andres Eduardo Coca Salazar, Liang Zhao","doi":"10.1109/BRACIS.2016.037","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.037","url":null,"abstract":"Musical scales play an important role in melodies, since its properties are reflected to the melodic essence. The extraction and understanding of scales are essential in both analysis and composition of music. However, the scale identification is a nontrivial task. Consequently, classic algorithms for identifying scales have been developed based on the most popular scales, such as major and minor scales. In this paper, we propose a comprehensive method for identifying musical scales, which allows to detect a wide range of scales beyond the traditional ones. Our method uses a deterministic walk through the nodes of a graph, where each node represents a valid interval structure. The transition between nodes is performed following a validation rule that governs the fragmentation of intervals. Moreover, if the scale is incomplete, possible structures can be determined and the scale is estimated according to the harmonic similarity percentage measure. The proposed method has been tested using a database of Finnish folk melodies and a data set of random melodies composed using rarely used scales. Experimental results show good performance of the proposed technique.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"140 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":"116245280","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
Ensembles with Clustering-and-Selection Model Using Evolutionary Algorithms 基于进化算法的聚类选择集成模型
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.086
L. Almeida, Pedro Sereno Galvao
{"title":"Ensembles with Clustering-and-Selection Model Using Evolutionary Algorithms","authors":"L. Almeida, Pedro Sereno Galvao","doi":"10.1109/BRACIS.2016.086","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.086","url":null,"abstract":"Ensembles of classifiers is a way to improve the performance of the approach with single classifiers. The idea is to find and combine a set of classifiers that are responsible for smaller and theoretically easier parts of a problem to solve, in other words, divide to conquer. Between the ensembles models, there is the clustering and selection in which the training data are clustering, and a classifier is built for each cluster found. An answer for an input data is given based on a distance to the available clusters that has an associated classifier. In this paper, the clustering and selection model is explored with the use of Evolutionary Algorithms to search clusters that optimize the ensemble's performance. Experiments are conducted with ten datasets and using recent advances in classification methods. The results achieved good and promising performances compared to classical clustering-and-selection model and other methods to build ensembles.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"162 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":"132356635","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
Fusion Approaches of Feature Selection Algorithms for Classification Problems 分类问题特征选择算法的融合方法
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.075
Jhoseph Jesus, D. Araújo, A. Canuto
{"title":"Fusion Approaches of Feature Selection Algorithms for Classification Problems","authors":"Jhoseph Jesus, D. Araújo, A. Canuto","doi":"10.1109/BRACIS.2016.075","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.075","url":null,"abstract":"The large amount of data produced by applications in recent years needs to be analyzed in order to extract valuable underlying information from them. Machine learning algorithms are useful tools to perform this task, but usually it is necessary to reduce complexity of data using feature selection algorithms. As usual, many algorithms were proposed to reduce dimension of data, each one with its own advantages and drawbacks. The variety of algorithms leads to either choose one algorithm or to combine several methods. The last option usually brings better performance. Based on this, this paper proposes an analysis of two distinct approaches of combining feature selection algorithms (decision and data fusion). This analysis was made in supervised classification context using real and synthetic datasets. Results showed that one proposed approach (decision fusion) has achieved the best results for the majority of datasets.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"20 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":"134523610","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
Reasoning about Trust and Belief in Possibilistic Answer Set Programming 可能性答案集规划中信任与信念的推理
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.048
Gabriel Maia, João F. L. Alcântara
{"title":"Reasoning about Trust and Belief in Possibilistic Answer Set Programming","authors":"Gabriel Maia, João F. L. Alcântara","doi":"10.1109/BRACIS.2016.048","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.048","url":null,"abstract":"The Possibilistic Answer Set Framework was conceived to deal with not only non monotonic reasoning, but also with uncertainty by associating a certainty level to each piece of knowledge. Here we extend this formalism to a multiagent approach robust enough to manage both the uncertainty about autonomous agents expressed in terms of degrees of trust and the possibilistic uncertainty about their knowledge bases expressed as possibilistic answer set programs. As result, we have a decentralized system able to reason about trust and beliefs in an integrated way. Then we motivate its behavior on an example and highlight how our proposal can be employed to make decisions when the information is distributed, uncertain, potentially contradictory and not necessarily reliable.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"62 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":"126203952","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
A Voronoi Diagram Based Classifier for Multiclass Imbalanced Data Sets 基于Voronoi图的多类不平衡数据集分类器
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.030
Evandro J. R. Silva, C. Zanchettin
{"title":"A Voronoi Diagram Based Classifier for Multiclass Imbalanced Data Sets","authors":"Evandro J. R. Silva, C. Zanchettin","doi":"10.1109/BRACIS.2016.030","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.030","url":null,"abstract":"The imbalance problem is receiving an increasing attention in the literature. Studies in binary cases are recurrent, however there still are several real world problems with more than two classes. The known solutions for binary datasets may not be applicable in this case. Some efforts are being applied in decomposition techniques which transforms a multiclass problem into some binary problems. However it is also possible to face a multiclass problem with an ad hoc approach, i.e., a classifier able to handle all classes at once. In this work a method able to handle several classes is proposed. This new method is based on the Voronoi diagram. We try to dynamically divide the feature space into several regions, each one assigned to a different class. It is expected for the method to be able to construct a complex classification model. However, as it is in its beginning, some tests need to be performed in order to evaluate its feasibility. Experiments with some classical classifiers confirm its feasibility, and comparisons with ad hoc methods found in literature show its potentiality.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"25 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":"129092151","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
Discovery Biological Motifs Using Heuristics Approaches 使用启发式方法发现生物基序
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.041
J. C. Garbelini, A. Kashiwabara, D. Sanches
{"title":"Discovery Biological Motifs Using Heuristics Approaches","authors":"J. C. Garbelini, A. Kashiwabara, D. Sanches","doi":"10.1109/BRACIS.2016.041","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.041","url":null,"abstract":"The identification of transcription factors binding sites (TFBS) – also called motifs – in DNA sequences is the first step to understanding how works gene regulation. Recognizing these patterns in the promoter regions of co-expressed genes is a determining key for this. Although there are several algorithms for this purpose, the problem is still far from being solved because of the great diversity of gene expression and the binding sites low specificity. State of the art algorithms have limitations, such as the high number of false positives and low accuracy for Identifying weak motifs. In this article we proposed a new approach based on memetic algorithms (DMMA) for discovery mofifs. The proposed approach was developed using evolutionary computation along with the local search algorithms simulated annealing and variable neighborhood search. To attest the algorithm ability, tests were conducted in four datasets - two real and two synthetic - and the results were compared with other approaches in the literature.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"17 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":"132502696","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
Allocating Social Goals Using the Contract Net Protocol in Online Multi-agent Planning 基于契约网协议的在线多智能体规划社会目标分配
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.045
R. C. Cardoso, Rafael Heitor Bordini
{"title":"Allocating Social Goals Using the Contract Net Protocol in Online Multi-agent Planning","authors":"R. C. Cardoso, Rafael Heitor Bordini","doi":"10.1109/BRACIS.2016.045","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.045","url":null,"abstract":"Centralised planning systems generally assign goals to agents during the search for a solution to a planning problem. In a distributed multi-agent setting, this would constrain the autonomy of the agents, and violate their privacy. Thus, by using task allocation protocols, the agents themselves can compete to decide who will take each goal, then later plan individually provided coordination mechanisms are in place, giving a higher degree of autonomy and privacy during the planning process. In this paper, we propose the use of a contract net protocol mechanism to allocate social goals in multi-agent planning. Our contributions are an algorithm for determining a bid, and several bid evaluation criteria. We also define some heuristics that can be used as bids. In our algorithm, agents expand social goals using their plan library in order to collect information and formulate their bid. We also show the results of experiments in a multi-agent planning domain in order to evaluate our heuristics.","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":"130828747","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: 7
Hyper-Parameter Tuning of a Decision Tree Induction Algorithm 决策树归纳算法的超参数调优
2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.018
R. G. Mantovani, Tomáš Horváth, R. Cerri, J. Vanschoren, A. Carvalho
{"title":"Hyper-Parameter Tuning of a Decision Tree Induction Algorithm","authors":"R. G. Mantovani, Tomáš Horváth, R. Cerri, J. Vanschoren, A. Carvalho","doi":"10.1109/BRACIS.2016.018","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.018","url":null,"abstract":"Supervised classification is the most studied task in Machine Learning. Among the many algorithms used in such task, Decision Tree algorithms are a popular choice, since they are robust and efficient to construct. Moreover, they have the advantage of producing comprehensible models and satisfactory accuracy levels in several application domains. Like most of the Machine Leaning methods, these algorithms have some hyper-parameters whose values directly affect the performance of the induced models. Due to the high number of possibilities for these hyper-parameter values, several studies use optimization techniques to find a good set of solutions in order to produce classifiers with good predictive performance. This study investigates how sensitive decision trees are to a hyper-parameter optimization process. Four different tuning techniques were explored to adjust J48 Decision Tree algorithm hyper-parameters. In total, experiments using 102 heterogeneous datasets analyzed the tuning effect on the induced models. The experimental results show that even presenting a low average improvement over all datasets, in most of the cases the improvement is statistically significant.","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":"130904383","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: 62
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