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...

Statistical techniques for aggressive feature reduction are studied on data obtained in a gene knock-out experiment. The essential part of the process is automatic assessment of the quality of various feature selection methods. This is done by comparison of the performance of discriminating models built on candidate subsets of features. Experiments show that typical settings of popular 2-class ...

During the past decades, to study high-dimensional data in a large variety of problems, researchers have proposed many Feature Extraction algorithms. One of the most effective approaches for optimal feature extraction is based on mutual information (MI). However it is not always easy to get an accurate estimation for high dimensional MI. In terms of MI, the optimal feature extraction is creatin...

MOTIVATION Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohor...

Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categori...

The Support Vector Machine (SVM) (Cortes and Vapnik,1995; Vapnik, 1995; Burges, 1998) is intended to generate an optimal separating hyperplane by minimizing the generalization error without the assumption of class probabilities such as Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called Support Vectors (SVs). In practice, these SVMs a...

Modelling varying speaking style remains a challenge to state of the art speech recognition and synthesis systems. Vowel and consonant reduction have been identified as correlative to speaking style variation, but still lack a common measurement. The reduction phenomena are often observed without consideration of coarticulation and assimilation effects, and as a result of speaking rate variabil...

In statistical classification work, one method of speeding up the process is to use only a small percentage of the total parameter set available. In this paper, we apply this technique both to the classification of malware and the identification of malware from a set combined with cleanware. In order to demonstrate the usefulness of our method, we use the same sets of malware and cleanware as i...

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This paper presents and compares three feature reduction techniques that were applied to Arabic text. The techniques include stemming, light stemming, and word clusters. The effects of the aforementioned techniques were studied and analyzed on the K-nearest-neighbor classifier. Stemming reduces words to their stems. Light stemming,by comparison, removes commonaffixes from words without reducing...