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 booklet and its significant other quantity, LNCS vol. 8794 and 8795 represent the lawsuits of the fifth foreign convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers awarded have been rigorously reviewed and chosen from 198 submissions. The papers are equipped in 18 cohesive sections, three designated periods and one aggressive consultation protecting all significant subject matters of swarm intelligence study and improvement resembling novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for vacationing salesman challenge; man made bee colony algorithms; synthetic immune method; evolutionary algorithms; neural networks and fuzzy equipment; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; type equipment; GPU-based equipment; scheduling and course making plans; instant sensor networks; energy procedure optimization; swarm intelligence in picture and video processing; functions of swarm intelligence to administration difficulties; swarm intelligence for real-world application.

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Extra info for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II

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The experimental results presented in this paper indicate that the studied metrics might be not appropriate in situations where multiple minority and multiple majority classes exist. Keywords: Metrics, Multi-class Imbalance, Multiple Minority and Majority Classes. 1 Introduction Class imbalance problems have drawn growing interest recently because of their classification difficulty caused by the imbalanced class distributions [8]. So, it has been into the 10 challenging problems identified in data mining research [9].

ICSI 2014, Part II, LNCS 8795, pp. 24–33, 2014. © Springer International Publishing Switzerland 2014 A Novel Rough Set Reduct Algorithm to Feature Selection Based on AFSA 25 informative features and remove all other attributes from the feature set with minimal information loss [4]. Rough set is a powerful mathematical tool to reduce the number of features based on the degree of dependency between condition attributes and decision attributes, which has been widely applied in many fields such as machine learning and data mining.

The elements of feature core are those features that cannot be eliminated. In this paper, the algorithm for finding feature core is as follows: initialize Core = ∅ ; for every attribute a ∈ C , if μC −{a} (D) < μC ( D) , then attribute a is one element of feature core, namely Core = Core ∪ {a} . Where μC ( D) represents the degree of dependency between condition attributes C and decision attribute D. The quick reduct (QR) algorithm proposed in [6], attempts to obtain a reduct without exhaustively generating all possible subsets.

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