By Ying Tan, Yuhui Shi, Carlos A Coello Coello
This booklet and its better half 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 awarded have been rigorously reviewed and chosen from 198 submissions. The papers are equipped in 18 cohesive sections, three specified periods and one aggressive consultation masking all significant themes of swarm intelligence examine and improvement comparable to 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; man made immune procedure; evolutionary algorithms; neural networks and fuzzy equipment; hybrid equipment; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; category tools; GPU-based equipment; scheduling and course making plans; instant sensor networks; energy process optimization; swarm intelligence in snapshot and video processing; purposes of swarm intelligence to administration difficulties; swarm intelligence for real-world application.
Read Online or Download Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II PDF
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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
The essence of rough set to feature selection are to find a minimal subset of the original features with the most Y. Tan et al. ): 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 . 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.
Here the results of GA are exported which are optimized weights and bias of ANN and ANN is run for 10,000 epochs. The result of this is passed through various integrators. We then experiment ensemble model with various integration methods to find an optimized parameter which gives best performance. After getting the optimized parameter, the detection procedure is run 20 times for the same configuration. After this, the mean and standard deviation are computed. The mean is taken as the performance accuracy of the system for training and testing dataset.
The Breast Cancer data set has vectors with a total of 30 input attributes. This database contains information about 569 patients with 212 out of 569 having malignant tumors. They are measured for a total of 3 cells. The various integration methods are compared with respect to their ability to train, learn and generalize the data. One with the best generalizing capacity will give the best detection efficiency. First we divide the data set into training and testing sets at 70% and 30% by taking 398 vectors as training data set and rest as testing data set.