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This publication and its spouse quantity, LNCS vol. 8794 and 8795 represent the complaints of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers provided have been rigorously reviewed and chosen from 198 submissions. The papers are equipped in 18 cohesive sections, three targeted periods and one aggressive consultation masking all significant subject matters of swarm intelligence study and improvement akin to novel swarm-based seek tools; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; synthetic bee colony algorithms; synthetic immune procedure; evolutionary algorithms; neural networks and fuzzy tools; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; class tools; GPU-based tools; scheduling and course making plans; instant sensor networks; energy method 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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Additional info for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II
1 Introduction Feature selection is the process of choosing a good subset of relevant features and eliminating redundant ones from an original feature set, which can be perceived as a principal pre-processing tool for solving the classification problem . The main objective of feature selection is to find a minimal feature subset from a set of features with high performance in representing the original features . In classification problems, feature selection is a necessary step due to lots of irrelevant or redundancy features.
In order to find the optimal reduct and improve the performance, a variety of search techniques hybrid with rough set are introduced to address feature selection problems such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO). These swarm intelligence based algorithms such as particle swarm and ant colony optimization have been proved to be competitive in rough set attribute reduction fields. However, these algorithms have some disadvantages such as premature convergence in PSO and the performance of the reduct depending on initial parameters in ACO.
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. Tecnol´ogico s/n Ex-Rancho La Virgen, 52140, Metepec, M´exico Abstract. In this paper we study some of the most common assessment metrics employed to measure the classifier performance on the multi-class imbalanced problems. The goal of this paper is empirically analyzing the behavior of these metrics on scenarios where the dataset contains multiple minority and multiple majority classes.