Download Advances in Swarm Intelligence: 5th International by Ying Tan, Yuhui Shi, Carlos A Coello Coello PDF

By Ying Tan, Yuhui Shi, Carlos A Coello Coello

This e-book and its spouse quantity, LNCS vol. 8794 and 8795 represent the court cases of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers offered have been rigorously reviewed and chosen from 198 submissions. The papers are equipped in 18 cohesive sections, three detailed classes and one aggressive consultation masking all significant themes of swarm intelligence examine and improvement reminiscent of novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; synthetic bee colony algorithms; man made immune approach; evolutionary algorithms; neural networks and fuzzy equipment; hybrid tools; multi-objective optimization; multi-agent platforms; evolutionary clustering algorithms; category tools; GPU-based equipment; scheduling and course making plans; instant sensor networks; strength approach optimization; swarm intelligence in snapshot and video processing; functions of swarm intelligence to administration difficulties; swarm intelligence for real-world application.

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IEEE Internet Computing 12(6), 37–49 (2008) 5. : Temporally adaptive estimation of logistic classifiers on data streams. Advances in Data Analysis and Classification 3(3), 243–261 (2009) 6. : On the window size for classification in changing environments. Intelligent Data Analysis 13(6), 861–872 (2009) 7. : Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams. In: IEEE 10th International Conference on Data Mining (ICDM), pp. 1175–1180 (2010) 8. : Enabling fast prediction for ensemble models on data streams.

17–23, 2014. P. S´anchez-Crisostomo et al. Often in research where the multi-class imbalance is the focus of study, the authors use metrics that have been extended from two-class imbalance scenarios to compare the classifier performance, for example the geometric mean, F-measure or measures of the area under curve family [2]. However, this situation presents an interesting research question: The proposed metrics to assessment the classifier performance are appropriate on scenarios where exist multiple minority and multiple majority classes?.

A. ) Coevolutionary Fuzzy Modeling. LNCS, vol. 3204, pp. 71–87. Springer, Heidelberg (2004) Empirical Analysis of Assessments Metrics for Multi-class Imbalance Learning on the Back-Propagation Context Juan Pablo S´anchez-Crisostomo1, Roberto Alejo1 , Erika L´opez-Gonz´alez1, Rosa Mar´ıa Valdovinos2, and J. Horacio Pacheco-S´anchez3 1 2 Tecnol´ogico de Estudios Superiores de Jocotitl´an, Carretera Toluca-Atlacomulco KM. 8, Col. P. 50100, Toluca, Estado de M´exico 3 Instituto Tecnol´ogico de Toluca Av.

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