Adaptive configuration of radial basis function network by regression tree allied with hybrid particle swarm optimization algorithm

Abstract Configuration of a radial basis function network (RBFN) comprises identifying the network parameters (inputs, centers as well as widths in RBF units, and weights between the hidden and output layers) and architecture. The issues of overfitting and local optima often happened during RBFN training. To rectify this situation, regression tree (RT), allied with hybrid particle swarm optimization (PSO) algorithm, was invoked to configure an RBFN to form the HPSORTRBFN algorithm in the present study. Discrete PSO was invoked to obtain an RT of the right size. The regions in the instance space defined by the leaf nodes of the grown RT were transformed into the centers in RBF units and the number of leaf nodes acted as the network structure. The splitting variables in RT became the inputs in RBFN. The widths and weights in RBFN were simultaneously optimized by continuous PSO. HPSORTRBFN was applied to predict the anti-HIV activities of 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) analogues and the bioactivities of flavonoid derivatives. The results showed RT allied with HPSO is able to configure a globally optimal RBFN and HPSORTRBFN owns superior modeling performance to RBFN and RT.

[1]  Sung Yang Bang,et al.  An Efficient Method to Construct a Radial Basis Function Neural Network Classifier , 1997, Neural Networks.

[2]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[3]  Jaroslaw Polanski,et al.  A 4D-QSAR study on anti-HIV HEPT analogues. , 2006, Bioorganic & medicinal chemistry.

[4]  Desire L. Massart,et al.  Detection of inhomogeneities in sets of NIR spectra , 1996 .

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Desire L. Massart,et al.  Detection of nonlinearity in multivariate calibration , 1998 .

[7]  Mohamad T. Musavi,et al.  On the training of radial basis function classifiers , 1992, Neural Networks.

[8]  Jian-Hui Jiang,et al.  Adaptive Configuring of Radial Basis Function Network by Hybrid Particle Swarm Algorithm for QSAR Studies of Organic Compounds , 2006, J. Chem. Inf. Model..

[9]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[10]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[11]  John Hallam,et al.  Combining Regression Trees and Radial Basis Function Networks , 2000, Int. J. Neural Syst..

[12]  Ru-Qin Yu,et al.  Hybridized particle swarm algorithm for adaptive structure training of multilayer feed‐forward neural network: QSAR studies of bioactivity of organic compounds , 2004, J. Comput. Chem..

[13]  Jian-Hui Jiang,et al.  Optimized Partition of Minimum Spanning Tree for Piecewise Modeling by Particle Swarm Algorithm. QSAR Studies of Antagonism of Angiotensin II Antagonists , 2004, J. Chem. Inf. Model..

[14]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[15]  A. Thakur,et al.  QSAR study of flavonoid derivatives as p56lck tyrosinkinase inhibitors. , 2004, Bioorganic & medicinal chemistry.

[16]  M. Kubat,et al.  Decision trees can initialize radial-basis function networks , 1998, IEEE Trans. Neural Networks.

[17]  Jooyoung Park,et al.  Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.

[18]  Stephen A. Billings,et al.  Radial basis function network configuration using genetic algorithms , 1995, Neural Networks.

[19]  Jian Jiao,et al.  Modified Particle Swarm Optimization Algorithm for Adaptively Configuring Globally Optimal Classification and Regression Trees , 2009, J. Chem. Inf. Model..

[20]  Shengrui Wang,et al.  Image classification algorithm based on the RBF neural network and K-means , 1998 .

[21]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[22]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[23]  Peter de B. Harrington,et al.  Self-Configuring Radial Basis Function Neural Networks for Chemical Pattern Recognition , 1999, J. Chem. Inf. Comput. Sci..

[24]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[25]  Wei-Qi Lin,et al.  Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies. , 2007, Talanta.