An Evolutionary Pentagon Support Vector Finder Method

In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results

An enhanced artificial bee colony algorithm based on fitness weighted search strategy

Artificial Bee Colony (ABC) algorithm is a meta-heuristic algorithm, is inspired by the bee’s food search behaviour based on swarm intelligence. Successful applications were performed on many optimization problems using this algorithm rising in popularity over the past few years. The update mechanism of the ABC algorithm, despite the fact that its exploration is good, faces the problem of convergence performance. For solving convergence problem of ABC Algorithm An Artificial Bee Colony Algorithm Based Fitness Weighted Search (ABCFWS) algorithm proposed in this paper. In this approach, an intelligent search space is proposed instead of the random search space of the ABC algorithm. In this method, the fitness values of the food source and the selected neighbour food source are taken as weights and a more balanced search space was found in the direction of the food source with better fitness value. The proposed method has been applied to 28 unconstrained numerical optimization test problems with different characteristics and the results were compared with the ABC algorithm variations. The results show that the proposed method is successful and competitive

Una propuesta de algoritmo evolutivo de inspiración cuántica para representación real usando filtro de partículas

En este trabajo se propone, implementa y evalu´a el modelo Quantum Inspired Evolutionary Algorithm with Real Representation using Filter Particle (FP-QIEA-R); este modelo usa la generacio´n cla´sica del modelo Quantum Inspired Evolutionary Algorithm with Real Representation (QIEA-R) (uso de funcio´n de distribucio´n de probabilidad uniforme) y propone la generacio´n cl´asica usando un mecanismo inspirado en filtro de part´ıculas, aproximaci´on de funciones, recompensa de los mejores individuos y muestreo usando funciones de distribucio´n de probabilidad para la bu´squeda global y centroides para la bu´squeda local. Durante el progreso de este trabajo fueron evaluados varios m´etodos de estimacio´n de funciones: uni-dimensionales (splines, interpolaci´on de akima), multi-dimensionales (regresio´n multilineal, parzen window) para estimar la funcio´n de distribucio´n acumulada(modificada usando el criterio de recompensa). Para evaluar el modelo, se realizaron experimentos con funciones benchmark (Ackley, Rastrigin, Rosenbrock, Schwefel, Sphere) usando una dimensionalidad de 30 y 100. Algunas aplicaciones reales fueron evaluadas: la inicializaci´on de una red perceptr´on multicapa para ayudar la convergencia(reducir el nu´mero de ´epocas), encontrar los ´angulos en el problema de desdoblamiento de prote´ınas. En los primeros experimentos, todos los modelos fueron comparados usando medidas estad´ısticas(media,desviaci´on est´andar), tiempo de ejecucio´n y de acuerdo a los resultados obtenidos el modelo m´as robusto fue el modelo que usa interpolacio´n de akima y an˜ade durante las generaciones a los mejores individuos. Los resultados obtenidos mostraron que la propuesta tiene el mejor desempen˜o tratando diversos problemas de optimizaci´on num´erica comparado con el modelo existente QIEA-R.Tesi

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy

Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy

14 págs.; 10 figs.; 7 tabs.; 1 app.© 2014 Elsevier B.V. Besides the well-established healthy properties of pollen, palynology and apiculture are of extreme importance to avoid hard and fast unbalances in our ecosystems. To support such disciplines computer vision comes to alleviate tedious recognition tasks. In this paper we present an applied study of the state of the art in pattern recognition techniques to describe, analyze, and classify pollen grains in an extensive dataset specifically collected (15 types, 120 samples/type). We also propose a novel contour-inner segmentation of grains, improving 50% of accuracy. In addition to published morphological, statistical, and textural descriptors, we introduce a new descriptor to measure the grain's contour profile and a logGabor implementation not tested before for this purpose. We found a significant improvement for certain combinations of descriptors, providing an overall accuracy above 99%. Finally, some palynological features that are still difficult to be integrated in computer systems are discussed.This work has been supported by the European project APIFRESH FP7-SME-2008-2 ‘‘Developing European standards for bee pollen and royal jelly: quality, safety and authenticity’’ and we would like to thank to Mr. Walter Haefeker, President of the European Professional Beekeepers Association (EPBA). J. Victor Marcos is a ‘‘Juan de la Cierva’’ research fellow funded by the Spanish Ministry of Economy and Competitiveness. Rodrigo Nava thanks Consejo Nacional de Ciencia y Tecnología (CONACYT) and PAPIIT Grant IG100814.Peer Reviewe

Score Fusion Using Hybrid Bacterial Foraging Optimization And Particle Swarm Optimization (Bfo-Pso) For Hand-Based Multimodal Biometrics

In recent times of biometric authentication, the influence of swarm intelligence algorithms role-played in enhancing the performance accuracy to a greater extent. Most researches related to Swarm Intelligence (SI) algorithms have done on the particular, due to the need to integrate more than one SI algorithm for better results. Therefore, this research is focused on the hand-based multimodal biometric score fusion which incorporates the scores of hand-based multimodalities and the optimal weights using Hybrid Bacterial Foraging - Particle Swarm Optimization (HBF-PSO) algorithm

Fault Detection by Signal Reconstruction in Nuclear Power Plants

In this work, the recently developed auto associative bilateral kernel regression (AABKR) method for on-line condition monitoring of systems, structures, and components (SSCs) during transient process operation of a nuclear power plant (NPP) is improved. The advancement enhances the capability of reconstructing abnormal signals to the values expected in normal conditions during both transient and steady-state process operations. The modification introduced to the method is based on the adoption of two new approaches using dynamic time warping (DTW) for the identification of the time position index (the position of the nearest vector within the historical data vectors to the current on-line query measurement) used by the weighted-distance algorithm that captures temporal dependences in the data. Applications are provided to a steady-state numerical process and a case study concerning sensor signals collected from a reactor coolant system (RCS) during start-up operation of a NPP. The results demonstrate the effectiveness of the proposed method for fault detection during steady-state and transient operations