Paper Info
Title
A New Breakpoint In Hybrid Particle Swarm-Neural Network Architecture: Individual Boundary Adjustment
Abstract
Neural Network (NN) is an effective classifier, but it generally uses the Backpropagation type algorithms which are insufficient because of trapping to local minimum of error rate. For elimination of this handicap, stochastic optimization algorithms are used to update the parameters of NN. Particle Swarm Optimization (PSO) is one of these providing a robust coherence with NN. In realized studies about Hybrid PSO-NN, position and velocity boundaries of weight and bias are chosen equal or set free in space which leave the performance of PSO-NN in suspense. In this paper, the limitations of weight velocity (wv), weight position (wp), bias velocity (bv) and bias position (bp) are diversely changed and their effects on the output of hybrid structure are examined. Concerning this, the formed structure is called as Bounded PSO-NN on account of adjusting the optimum operating conditions (intervals). On performance evaluation, proposed method is tested on binary and multiclass pattern classiffication by using six medical datasets: Wisconsin Breast Cancer (WBC), Pima Indian Diabetes (PID), Bupa Liver Disorders (BLD), Heart Statlog (HS), Breast Tissue (BT) and Dermatology Data (DD). Upon analyzing the results, it was revealed that Bounded PSO-NN has a faster processing time than general PSO-NNs in which set-free and wpi=bpi and wvi=bvi conditions are settled. The superiority in terms of processing time is about 199 s (set-free) and 307 s (wpi=bpi and wvi=bvi) for training, about 16 ms (set-free) and 9ms (wpi=bpi and wvi=bvi) for test. In terms of classification performance, PSO-NN (set-free condition), PSO-NN (wpi=bpi & wvi=bvi) and PSO-NN with individual boundary adjustment (bounded PSO-NN) respectively achieves to accuracy rates as 69.84%, 95.31% and 97.22% on WBC, 47.01%, 76.69% and 77.73% on PID, 55.36%, 67.54% and 73.91% on BLD, 64.82%, 81.48% and 85.56% on HS, 75%, 92.31% and 100% on BT, 27.47%, 92.31% and 100% on DD. In the light of experiments, it is seen that Bounded PSO-NN is better than general PSO-NNs for obtaining the optimum results. Consequently, the importance of limitations is clarified and it is proven that each limitation must be adjusted individually, not be set free or not be chosen equal.
Year
DOI
Venue
2016
10.1142/S0219622016500395
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Keywords
Field
DocType
Particle swarm optimization, neural networks, hybrid classifier, position-velocity intervals, boundary adjustment
Data mining,Stochastic optimization,Artificial intelligence,Artificial neural network,Classifier (linguistics),Particle swarm optimization,PID controller,Word error rate,Algorithm,Statistics,Backpropagation,Machine learning,Mathematics,Bounded function
Journal
Volume
Issue
ISSN
15
6
0219-6220
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Rahime Ceylan125917.10
Hasen Koyuncu2144.84