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2018 7th Brazilian Conference on Intelligent Systems (BRACIS)最新文献

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Bandit-Based Automated Machine Learning 基于强盗的自动机器学习
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00029
S. N. D. Dôres, Carlos Soares, D. Ruiz
{"title":"Bandit-Based Automated Machine Learning","authors":"S. N. D. Dôres, Carlos Soares, D. Ruiz","doi":"10.1109/BRACIS.2018.00029","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00029","url":null,"abstract":"Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis.","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":"123852665","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: 8
[Copyright notice] (版权)
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/bracis.2018.00003","DOIUrl":"https://doi.org/10.1109/bracis.2018.00003","url":null,"abstract":"","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"574 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":"124254062","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
On Monotonic Tendency of Some Fuzzy Cluster Validity Indices for High-Dimensional Data 高维数据若干模糊聚类有效性指标的单调倾向
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00102
Fernanda Eustáquio, T. Nogueira
{"title":"On Monotonic Tendency of Some Fuzzy Cluster Validity Indices for High-Dimensional Data","authors":"Fernanda Eustáquio, T. Nogueira","doi":"10.1109/BRACIS.2018.00102","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00102","url":null,"abstract":"Fuzzy clustering validation of high-dimensional data sets is only possible using a reliable cluster validity index. Therefore, the selection of an index is as important as choosing an appropriate clustering algorithm. A good validity index is that one that correctly recognize the data structure by choosing its correct number of clusters, and it is not sensitive to any parameter of the clustering algorithm or data property. However, some classical fuzzy validity indices as Partition Coefficient (PC), Partition Entropy (PE) and Fukuyama-Sugeno (FS) are sensitive to the fuzzification factor m and the number of clusters c, both parameters of the well-known Fuzzy c-Means (FCM) algorithm. They present the monotonic tendency in function of c even varying the values of m: the PC and FS values become smaller when c increases and the opposite occurs with PE. Although the literature presents extensive investigations about such tendency, they were conducted for low-dimensional data, in which such data property does not affect the clustering behavior. In order to investigate how such aspects affect the fuzzy clustering results of high-dimensional data, in this work we have clustered objects of ten real high-dimensional data sets, using FCM validated by PC, PE, FS and some proposed modifications of them to lead with the monotonic tendency. The results showed that the Modified Partition Coefficient (MPC) is the more reliable index to validate fuzzy clustering of high-dimensional data.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"25 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":"121071180","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: 5
Weightless Neural Network WiSARD Applied to Online Recommender Systems 无重力神经网络在在线推荐系统中的应用
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00067
Kleyton Pontes Cotta, Raul Sena Ferreira, Felipe M. G. França
{"title":"Weightless Neural Network WiSARD Applied to Online Recommender Systems","authors":"Kleyton Pontes Cotta, Raul Sena Ferreira, Felipe M. G. França","doi":"10.1109/bracis.2018.00067","DOIUrl":"https://doi.org/10.1109/bracis.2018.00067","url":null,"abstract":"Recommender systems generally are made to predict user preferences' for items. However, in high dimensional datasets this task demands high computational costs. Taking into account that data distribution changes through time, it is important that online recommender systems have a fast retraining process in order to keep the model updated, delivering accurate predictions. Therefore, we propose a new approach for recommender systems using a weightless neural network, denominated WiSARD. We show that our proposal increases training and prediction processing speed, without decreasing the quality of predictions. First results show that our proposal is 306% faster than the improved regularized singular value decomposition (IRSVD), a well-known state-of-the-art algorithm. Moreover, our proposal still had an improvement of 3.7% regarding the mean absolute error (MAE). We show how to apply the WiSARD algorithm for online recommender systems, its drawbacks, and insights for further research.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"51 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":"115987450","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 New Adaptive Operator Selection for NSGA-III Applied to CEC 2018 Many-Objective Benchmark 应用于CEC 2018多目标基准的NSGA-III自适应算子选择
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00010
J. Kuk, Richard A. Gonçalves, C. Almeida, Sandra M. Venske, A. Pozo
{"title":"A New Adaptive Operator Selection for NSGA-III Applied to CEC 2018 Many-Objective Benchmark","authors":"J. Kuk, Richard A. Gonçalves, C. Almeida, Sandra M. Venske, A. Pozo","doi":"10.1109/BRACIS.2018.00010","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00010","url":null,"abstract":"As well as new algorithms are constantly proposed, new test functions for these algorithms are also designed. In this paper we explore 15 new benchmark functions proposed for CEC-2018 Multiobjective Evolutionary Algorithms (MOEA) Competition for many-objective optimization. The functions have diverse properties which cover a good representation of various real-world scenarios. We propose many-objective approaches that were designed considering three schemes to perform adaptive operator selection with NSGA-III algorithm: Thompson Sampling, Probability Matching and Adaptive Pursuit. They select from a pool of candidates composed by DE mutations and a Genetic Algorithm crossover. Thompson Sampling is a multi-armed bandit approach, i.e., it was designed to deal with the exploration versus exploitation dilemma intrinsic to the adaptive operator selection problem. Its use in a many objective evolutionary algorithm is innovative and constitutes the main contribution of this work. As the CEC-2018 is composed by complex, potentially nonlinear functions, we also perform the analysis of the effects of the insertion of a nonlinear operator within the candidate pool of operators. Statistical analysis of the experiments were performed with Mann-Whitney and Friedman tests. The IGD indicator was used to infer the quality of the solutions. The results indicate the use of Thompson Sampling as an adaptive operator selection is promising and increases the optimization performance of NSGA-III. They also indicate that the use of the nonlinear operator is capable of improving the results of all adaptive versions.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"34 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":"126347022","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: 8
Using LSTM Encoder-Decoder for Rhetorical Structure Prediction 用LSTM编解码器进行修辞结构预测
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/bracis.2018.00055
Gustavo Bennemann de Moura, Valéria Delisandra Feltrim
{"title":"Using LSTM Encoder-Decoder for Rhetorical Structure Prediction","authors":"Gustavo Bennemann de Moura, Valéria Delisandra Feltrim","doi":"10.1109/bracis.2018.00055","DOIUrl":"https://doi.org/10.1109/bracis.2018.00055","url":null,"abstract":"The importance of identifying rhetorical categories in texts has been widely acknowledged in the literature, since information regarding text organization or structure can be applied in a variety of scenarios, including genre-specific writing support and evaluation, both manually and automatically. In this paper we present a Long Short-Term Memory (LSTM) encoder-decoder classifier for scientific abstracts. As a large corpus of annotated abstracts was required to train our classifier, we built a corpus using abstracts extracted from PUBMED/MEDLINE. Using the proposed classifier we achieved approximately 3% improvement in per-abstract accuracy over the baselines and 1% improvement for both per-sentence accuracy and f1-score.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"49 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":"121690251","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
Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem 基于多臂班组的超启发式置换流水车间问题
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00032
C. Almeida, Richard A. Gonçalves, Sandra M. Venske, R. Lüders, M. Delgado
{"title":"Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem","authors":"C. Almeida, Richard A. Gonçalves, Sandra M. Venske, R. Lüders, M. Delgado","doi":"10.1109/BRACIS.2018.00032","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00032","url":null,"abstract":"In this work, we propose MAB variants as selection mechanisms of a hyper-heuristic running on the multi-objective framework named MOEA/D-DRA to solve the Permutation Flow Shop Problem (PFSP). All the variants are designed to choose which of low-level heuristic components (for crossover and mutation operators) should be applied to each solution during execution. FRRMAB is the classical MAB, RMAB is restless and LinUCB is contextual (its context is based on side information). The proposed approaches are compared with each other and the best one, MOEA/D-LinUCB, is compared with MOEA/DDRA using the hypervolume indicator and nonparametric statistical tests. The results demonstrate the robustness of MAB-based approaches, especially the contextual-based one.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"66 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":"121928388","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: 8
Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem 为MaxSAT问题推荐元启发式的元学习
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00037
Enrico S. Miranda, F. Fabris, Chrystian G. M. Nascimento, A. Freitas, A. Oliveira
{"title":"Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem","authors":"Enrico S. Miranda, F. Fabris, Chrystian G. M. Nascimento, A. Freitas, A. Oliveira","doi":"10.1109/BRACIS.2018.00037","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00037","url":null,"abstract":"It is of great interest to build recommendation systems capable of choosing the best solver for a particular problem of a combinatorial optimisation task given past runs of solvers in various problems of that optimisation task. In this paper, a meta-learning approach is proposed to predict which metaheuristic is the best solver for MaxSAT problems. The proposal includes the creation of new meta-features derived from graph descriptions of MaxSAT problems and an interpretation of the meta-model. Our approach successfully selected the best metaheuristic to solve each problem in 87% of the cases. Also, the new meta-features have shown to be as good as the state-of-the-art meta-features, and the meta-model interpretation found interesting problem-specific knowledge.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"147 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":"127511655","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
Quantum Enhanced k-fold Cross-Validation 量子增强k-fold交叉验证
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00041
P. D. Santos, Ismael C. S. Araújo, Rodrigo S. Sousa, A. J. D. Silva
{"title":"Quantum Enhanced k-fold Cross-Validation","authors":"P. D. Santos, Ismael C. S. Araújo, Rodrigo S. Sousa, A. J. D. Silva","doi":"10.1109/BRACIS.2018.00041","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00041","url":null,"abstract":"In this work, we propose a quantum-classical algorithm able to perform a k-fold cross-validation with linear speedup. The proposed method creates a quantum superposition with patterns from a dataset and a classifier can evaluate all patterns at once. We used a probabilistic quantum memory in order to conduct the performance evaluation. The proposed method was verified through a reduced experimental analysis conducted classically.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"198 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":"134152169","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
Detecting Fake Suppliers using Deep Image Features 利用深度图像特征检测假冒供应商
2018 7th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2018-10-01 DOI: 10.1109/BRACIS.2018.00046
Jonas Wacker, R. Ferreira, M. Ladeira
{"title":"Detecting Fake Suppliers using Deep Image Features","authors":"Jonas Wacker, R. Ferreira, M. Ladeira","doi":"10.1109/BRACIS.2018.00046","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00046","url":null,"abstract":"The Observatory of Public Spending (ODP, in Portuguese) is a special unit of Brazil's Ministry of Transparency and Office of the Comptroller-General (CGU, in Portuguese) responsible for gathering managerial and audit information to support the work of its auditors. One of the most important tasks of this unit is to monitor government suppliers who have won procurement processes. Image analysis of the location of many of these suppliers revealed suspicious scenes, such as rural areas, isolated places or slums. These scenes could be an indicator of fake suppliers with poor capacity of delivering public goods. However, checking thousands of images in order to find suspicious suppliers would be very expensive. Our objective is to automatically distinguish images of valid supplier locations from arbitrary buildings and landscapes. We extract deep features from a collection of Google Street View images using a pretrained convolutional neural network (Places CNN) to classify supplier locations and show that these features can be well applied to the context of identifying valid suppliers, independent of the image perspective that was collected.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"77 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":"129990684","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
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