Measures for unsupervised fuzzy-rough feature selection

For supervised learning, feature selection algorithms at-tempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is therefore intuitive to re-tain only those features that are related to or lead to these decision classes. However, in unsupervised learning, deci-sion class labels are not provided, which poses questions such as; which features should be retained? and, why not use all of the information? The problem is that not all fea-tures are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, some new fuzzy-rough set-based approaches to unsupervised fea-ture selection are proposed. These approaches require no thresholding or domain information, can operate on real-valued data, and result in a significant reduction in dimen-sionality whilst retaining the semantics of the data. 1

Feature Selection Inspired Classifier Ensemble Reduction

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets. ? 2013 IEEE

Análisis estocástico de señales vibratorias de motores de inducción para la detección de fallas usando descomposición de modo empírico

This paper presents a vibration analysis on induction motors using Hidden Markov Models (HMM) applied to features obtained from the Empirical Mode Decomposition (EMD) and Hilbert-Huang transform to vibration signals obtained in the coordinates x and y, in order to detect malfunctions in bearings and bars. Additionally, a comparative analysis of the ability of the vibration signals in the x and y directions to provide information for failures detection is presented. Thus, an ergodic HMM initialized and trained by expectation maximization algorithm with convergence at 10e-7  and maximum iterations of 100 was applied to the feature space and its performance was determined by cross-validation with 80-20 with 30 fold for obtaining high performance fault detection in terms of accuracy.En este artículo se presenta un análisis de vibraciones en motores de inducción por medio de Modelos Ocultos de Markov (Hidden Markov Model - HMM) aplicado a características obtenidas de la Descomposición de Modo Empírico (Empirical Mode Decomposition - EMD) y transformada de Hilbert-Huang de señales de vibración obtenidas en las coordenadas x y y, con el fin de detectar fallas de funcionamiento en rodamientos y barras.  Además se presenta un análisis comparativo de la capacidad de las señales de vibración en dirección x y en dirección y, para aportar información en la detección de fallas. Así, un HMM ergódico inicializado y entrenado por medio del algoritmo de máxima esperanza, con convergencia en 10e-7 y un máximo de iteraciones de 100, se aplicó sobre el espacio de características y su desempeño fue determinado mediante validación cruzada 80-20 con 30 fold, obteniendo un alto desempeño para la detección de fallas en términos de exactitud