Sultan Noman Qasem

Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization (TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature.

Multi-objective hybrid evolutionary algorithms for radial basis function neural network design

This paper presents new multi-objective evolutionary hybrid algorithms for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithms are memetic Pareto particle swarm optimization based RBFN (MPPSON), Memetic Elitist Pareto non dominated sorting genetic algorithm based RBFN (MEPGAN) and Memetic Elitist Pareto non dominated sorting differential evolution based RBFN (MEPDEN). The proposed methods integrate accuracy and structure of RBFN simultaneously. These algorithms are implemented on two-class and multiclass pattern classification problems with one complex real problem. The results reveal that the proposed methods are viable, and provide an effective means to solve multi-objective RBFNs with good generalization ability and simple network structure. The accuracy and complexity of the network obtained by the proposed algorithms are compared through statistical tests. This study shows that the proposed methods obtain RBFNs with an appropriate balance between accuracy and simplicity.

Memetic Elitist Pareto Differential Evolution algorithm based Radial Basis Function Networks for classification problems

This paper presents a new multi-objective evolutionary hybrid algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, MEPDEN, Memetic Elitist Pareto evolutionary approach based on the Non-dominated Sorting Differential Evolution (NSDE) multi-objective evolutionary algorithm which has been adapted to design RBFNs, where the NSDE algorithm is augmented with a local search that uses the Back-propagation algorithm. The MEPDEN is tested on two-class and multiclass pattern classification problems. The results obtained in terms of Mean Square Error (MSE), number of hidden nodes, accuracy (ACC), sensitivity (SEN), specificity (SPE) and Area Under the receiver operating characteristics Curve (AUC), show that the proposed approach is able to produce higher prediction accuracies with much simpler network structures. The accuracy and complexity of the network obtained by the proposed algorithm are compared with Memetic Eilitist Pareto Non-dominated Sorting Genetic Algorithm based RBFN (MEPGAN) through statistical tests. This study showed that MEPDEN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other method considered.

Radial basis function Network based on multi-objective particle swarm optimization

The problem of unsupervised and supervised learning is discussed within the context of multi-objective optimization. In this paper, an evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structure are encoded into the particles in PSO. Then, they evolve toward Pareto-optimal front defined by several objective functions concerning with model accuracy and model complexity. This study suggests an approach of RBF Network training through simultaneous optimization of architectures and weights with PSO-based multi-objective algorithm. Our goal is to determine whether Multi-objective PSO can train RBF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on benchmark datasets obtained from the UCI machine learning repository. The results show that our proposed method provides an effective means for training RBF Networks that is competitive with other evolutionary computational-based methods.

Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization

This study proposes RBF Network hybrid learning with Particle Swarm Optimization for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections of weights between the hidden layer and the output layer. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation on dataset illustrate the effectiveness of PSO in enhancing RBF Network learning.

Improving performance of radial basis function network based with particle swarm optimization

In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. This study proposes hybrid learning of RBF Network with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. The hybrid learning of RBF Network involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in hybrid learning of RBF Network is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrate the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.

Improving generalization of radial basis function network with adaptive multi-objective particle swarm optimization

In this paper, an adaptive evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structures are encoded into particles in Particle Swarm Optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is discussed with Adaptive Multi-Objective PSO (AMOPSO). This study suggests an approach of RBF Network training through simultaneous optimization of architectures and weights with Adaptive PSO-based multi-objective algorithm. Our goal is to determine whether Adaptive Multi-objective PSO can train RBF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that our proposed method provides an effective means for training RBF Networks that is competitive with PSO-based multi-objective algorithm.