This work explores the use of Support Vector Machines (SVM) for topic classification of conversations. An All-vs-One SVM system is used as the baseline. Several methods in feature weight scaling and feature selection are compared. Results suggest that the conversation domain requires a different set of methods from the written text domain. Finally, a feature selection method based on hierarchic...

The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose novel feature selection method for support vector regression, called FS-NSVR, which first attempts solve the problem in regression technology field. FS-NSVR preserves representative features selected system due its use matrix original space. is challenging mixed-...

Land cover classification (LCC) in complex surface-mined landscapes has become very important for understanding the influence of mining activities on the regional geo-environment. There are three characteristics of complex surface-mined areas limiting LCC: significant three-dimensional terrain, strong temporal-spatial variability of surface cover, and spectral-spatial homogeneity. Thus, determi...

In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward’s with a distance measure based on GoodmanKruskal tau. We motivate the choice of this measure and compare it with other ones. Our hierarchical clustering is applied to over 40 data-sets from UCI archive. The proposed approach is interesting from many viewpoints. First, it pr...

In this paper we propose and test the use of hierarchical clustering for feature selection in databases. The clustering method is Ward’s with a distance measure based on Goodman-Kruskal τ . We motivate the choice of this measure and compare it with other ones. Our hierarchical clustering is applied to over 40 data-sets from UCI archive. The proposed approach is interesting from many viewpoints....

Feature selection for ensembles has shown to be an effective strategy for ensemble creation. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The first level performs feature selection in order to generate a set of good classifiers while the second one combines them to provide a set of powerful ensembles. The proposed met...

This work presents a hierarchical HMM-based audio segmentation system with feature selection designed for the Albayzin 2010 Evaluations. We propose an architecture that combines the outputs of individual binary detectors which were trained with a specific class-dependent feature set adapted to the characteristics of each class. A fast one-pass-training wrapper-based technique was used to perfor...

The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, wh...

this study investigates the detection of the drowsiness state for a future application such as in the reduction ofthe road traffic accidents. the electroencephalography(eeg), electrooculography (eog), driving quality (dq), and karolinska sleepiness scale (kss) data of 7 male during approximately 20 hours of sleep deprivation were recorded. to reduce the eye blink artifact, an automatic mechanis...

Classification on high-dimensional (i.e. thousands of dimensions) data typically requires feature selection (FS) as a pre-processing step to reduce the dimensionality. However, FS is a challenging task even on datasets with hundreds of features. This paper proposes a new particle swarm optimisation (PSO) based FS approach to classification problems with thousands or tens of thousands of feature...