A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data

All the higher order ANNs (HONNs) including functional link ANN (FLANN) are sensitive to random initialization of weight and rely on the learning algorithms adopted. Although a selection of efficient learning algorithms for HONNs helps to improve the performance, on the other hand, initialization of weights with optimized weights rather than random weights also play important roles on its efficiency. In this paper, the problem solving approach of the teaching learning based optimization (TLBO) along with learning ability of the gradient descent learning (GDL) is used to obtain the optimal set of weight of FLANN learning model. TLBO does not require any specific parameters rather it requires only some of the common independent parameters like number of populations, number of iterations and stopping criteria, thereby eliminating the intricacy in selection of algorithmic parameters for adjusting the set of weights of FLANN model. The proposed TLBO-FLANN is implemented in MATLAB and compared with GA-FLANN, PSO-FLANN and HS-FLANN. The TLBO-FLANN is tested on various 5-fold cross validated benchmark data sets from UCI machine learning repository and analyzed under the null-hypothesis by using Friedman test, Holm’s procedure and post hoc ANOVA statistical analysis (Tukey test & Dunnett test)

A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

In this paper, a Chemical Reaction Optimization (CRO) based higher order neural network with a single hidden layer called Pi–Sigma Neural Network (PSNN) has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others

A novel nature inspired firefly algorithm with higher order neural network: Performance analysis

The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information

A Global-best Harmony Search based Gradient Descent Learning FLANN (GbHS-GDL-FLANN) for data classification

While dealing with real world data for classification using ANNs, it is often difficult to determine the optimal ANN classification model with fast convergence. Also, it is laborious to adjust the set of weights of ANNs by using appropriate learning algorithm to obtain better classification accuracy. In this paper, a variant of Harmony Search (HS), called Global-best Harmony Search along with Gradient Descent Learning is used with Functional Link Artificial Neural Network (FLANN) for classification task in data mining. The Global-best Harmony Search (GbHS) uses the concepts of Particle Swarm Optimization from Swarm Intelligence to improve the qualities of harmonies. The problem solving strategies of Global-best Harmony Search along with searching capabilities of Gradient Descent Search are used to obtain optimal set of weight for FLANN. The proposed method (GbHS-GDL-FLANN) is implemented in MATLAB and compared with other alternatives (FLANN, GA based FLANN, PSO based FLANN, HS based FLANN, Improved HS based FLANN, Self Adaptive HS based FLANN, MLP, SVM and FSN). The GbHS-GDL-FLANN is tested on benchmark datasets from UCI Machine Learning repository by using 5-fold cross validation technique. The proposed method is analyzed under null-hypothesis by using Friedman Test, Holm and Hochberg Procedure and Post-Hoc ANOVA Statistical Analysis (Tukey Test & Dunnett Test) for statistical analysis and validity of results. Simulation results reveal that the performance of the proposed GbHS-GDL-FLANN is better and statistically significant from other alternatives