2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence最新文献

A Hybrid Algorithm for Solving the Economic Dispatch Problem 求解经济调度问题的混合算法

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.108

Raul Silva Barros, O. Cortes, R. Lopes, Josenildo Costa da Silva

引用次数: 8

Visual Odometry and Moving Objects Localization Using ORB and RANSAC in Aerial Images Acquired by Unmanned Aerial Vehicles 基于ORB和RANSAC的无人机航拍图像视觉里程测量与运动目标定位

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.79

R. A. Reboucas, Quenaz da Cruz Eller, Mateus Habermann, Elcio Hideiti Shiguemori

引用次数: 2

A New Algorithm Based on Differential Evolution for Combinatorial Optimization 一种基于差分进化的组合优化算法

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.21

André L. Maravilha, J. A. Ramírez, F. Campelo

引用次数: 5

Spatial Cognition Degree of Development Classification Using Artificial Neural Networks and Largest Lyapunov Exponents 基于人工神经网络和最大Lyapunov指数的空间认知发展程度分类

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.88

G. Maron, D. Barone, E. A. Ramos

引用次数: 1

Towards the Use of Metaheuristics for Optimizing the Combination of Classifier and Cluster Ensembles 使用元启发式优化分类器和聚类集成的组合

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.86

L. F. Coletta, Eduardo R. Hruschka, A. Acharya, Joydeep Ghosh

{"title":"Towards the Use of Metaheuristics for Optimizing the Combination of Classifier and Cluster Ensembles","authors":"L. F. Coletta, Eduardo R. Hruschka, A. Acharya, Joydeep Ghosh","doi":"10.1109/BRICS-CCI-CBIC.2013.86","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86","url":null,"abstract":"Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, we investigate how to make an existing algorithm, named C3E (from Combining Classifier and Cluster Ensembles), more user-friendly by automatically tunning its main parameters with the use of metaheuristics. In particular, the C3E algorithm is based on a general optimization framework that takes as input class membership estimates from existing classifiers, as well as a similarity matrix from a cluster ensemble operating solely on the new (target) data to be classified, and yields a consensus labeling of the new data. To do so, two parameters have to be defined a priori, namely: the relative importance of classifier and cluster ensembles and the number of iterations of the algorithm. In some practical applications, these parameters can be optimized via (time consuming) grid search approaches based on cross-validation procedures. This paper shows that metaheuristics can be more computationally efficient alternatives for optimizing such parameters. More precisely, analyses of statistical significance made from experiments performed on fourteen datasets show that five metaheuristics can yield classifiers as accurate as those obtained from grid search, but taking half the running time.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128167889","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

A WiSARD-Based Approach to CDnet 基于wisard的CDnet方法

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.37

Massimo De Gregorio, Maurizio Giordano

引用次数: 19

Comparing MOPSO Approaches for Hydrothermal Systems Operation Planning 热液系统运行规划的MOPSO方法比较

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.97

Jonathan Cardoso Silva, G. Cruz, C. Vinhal, D. R. C. Silva, C. Bastos-Filho

{"title":"Comparing MOPSO Approaches for Hydrothermal Systems Operation Planning","authors":"Jonathan Cardoso Silva, G. Cruz, C. Vinhal, D. R. C. Silva, C. Bastos-Filho","doi":"10.1109/BRICS-CCI-CBIC.2013.97","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.97","url":null,"abstract":"Hydrothermal operational planning is categorized as an optimization problem that demands operational strategies of hydroelectric power plants in order to minimize the use of thermoelectric power plants, while maintaining the highest possible level of system's reservoirs during planning period. Moreover, the problem must meet a set of complex constraints. We showed in this paper that it is possible to tackle the medium-term planning of hydrothermal systems as a multi-objective problem. The particles were represented as vectors indicating the monthly generation of hydropower. We applied some three recent swarm based multi-objective optimizers, MOPSO-CDR, MOPSO-DFR and SMPSO. This trade-off is presented in Pareto Fronts, which can be used for decision making. Among the assessed approaches involving a system composed of eight Brazilian hydroelectric plants, we observed that the MOPSO-CDR returned the best results and it is worth to include seeds from mono-objective approaches to improve the convergence capacity. We included the result achieved by the PSO-CLANM algorithm and it generated effective results.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134314292","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

High Level Classification Totally Based on Complex Networks 完全基于复杂网络的高级分类

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.90

M. Carneiro, Liang Zhao

{"title":"High Level Classification Totally Based on Complex Networks","authors":"M. Carneiro, Liang Zhao","doi":"10.1109/BRICS-CCI-CBIC.2013.90","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.90","url":null,"abstract":"Differently from traditional machine learning techniques applied to data classification, high level classification considers not only the physical features of the data (distance, similarity or distribution), but also the pattern formation of the data. In this latter case, a set of complex network measures are employed because of their abilities to capture spatial, functional and topological relations. Although high level techniques offer powerful features, good classification performance is usually obtained by combining them with some low level algorithms, which, in turn, reduces the efficiency of the overall technique. A priori, the reason is that low level and high level techniques provide different visions of classification. In this way, one cannot simply substitute another. This paper presents a data classification technique in which low level and high level classifications are embedded in a unique scheme, i.e., the proposed technique does not need a separated low level technique. The novelty is the use of a simple and recently proposed complex network measure, named component efficiency. Thus, our algorithm computes the efficiency of information exchanging among vertices in each component and the resulting values are used to drive the classification of the new instances i.e., a new instance will be classified into one of the components (class), in which their local features are in conformity with the insertion of the new instance. The experiments performed with artificial and real-world data sets show our approach totally based on complex networks is promising and it provides better results than some traditional classification techniques.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115803034","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: 9

Combined Active and Semi-supervised Learning Using Particle Walking Temporal Dynamics 结合主动和半监督学习的粒子行走时间动力学

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.14

Fabricio A. Breve

{"title":"Combined Active and Semi-supervised Learning Using Particle Walking Temporal Dynamics","authors":"Fabricio A. Breve","doi":"10.1109/BRICS-CCI-CBIC.2013.14","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.14","url":null,"abstract":"Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125747796","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

Learning Finite-State Machines with Classical and Mutation-Based Ant Colony Optimization: Experimental Evaluation 基于经典和基于突变的蚁群优化学习有限状态机:实验评价

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.93

D. Chivilikhin, V. Ulyantsev

引用次数: 2