Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Hybrid GA-SVM for Efficient Feature Selection in E-mail Classification

Feature selection is a problem of global combinatorial optimization in machine learning in which subsets of relevant features are selected to realize robust learning models. The inclusion of irrelevant and redundant features in the dataset can result in poor predictions and high computational overhead. Thus, selecting relevant feature subsets can help reduce the computational cost of feature measurement, speed up learning process and improve model interpretability. SVM classifier has proven inefficient in its inability to produce accurate classification results in the face of large e-mail dataset while it also consumes a lot of computational resources. In this study, a Genetic Algorithm-Support Vector Machine (GA-SVM) feature selection technique is developed to optimize the SVM classification parameters, the prediction accuracy and computation time. Spam assassin dataset was used to validate the performance of the proposed system. The hybrid GA-SVM showed remarkable improvements over SVM in terms of classification accuracy and computation time. Keywords: E-mail Classification, Feature-Selection, Genetic algorithm, Support Vector Machin

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011