Feature subset selection (FSS) has been an active area of research in machine learning. A number of techniques have been developed for selecting an optimal or sub-optimal subset of features, because it is a major factor to determine the performance of a machine-learning technique. In this paper, we propose and develop a novel optimization technique, namely, a binary coordinate ascent (BCA) algo...

Several effective machine learning and pattern recognition schemes have been developed for medical imaging. Although many classifiers have been used with computer-aided detection (CAD) for computed tomographic colonography (CTC), little is known about their relative performance. This pilot study compares the performance of several state-of-the-art classifiers and feature selection methods in th...

Feature selection is an essential preprocessing step for removing redundant or irrelevant features from multidimensional data to improve predictive performance. Currently, medical clinical datasets are increasingly large and not every feature helps in the necessary predictions. So, techniques used determine relevant set that can performance of a learning algorithm. This study presents analysis ...

Bistable perception emerges when a stimulus under continuous view is perceived as the alternation of two mutually exclusive states. Such a stimulus provides a unique opportunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this paper we analyze the dynamic activity of local field potential (LFP), simultaneously collected...

In this paper we propose an optimisation technique to choose a user independent feature subset from the input feature set for a DTW-based text-dependent speaker verification system. The optimisation technique is based on the l-r algorithm, which in essence is the combination of sequential forward and backward search algorithms. The performance criterion used for optimum feature selection is the...

Feature selection is a data processing method which aims to select effective feature subsets from original features. based on evolutionary computation (EC) algorithms can often achieve better classification performance because of their global search ability. However, methods using EC cannot get rid invalid features effectively. A small number still exist till the termination algorithms. In this...

Feature selection is an important part of the process of text classification, there is a direct impact on the quality of feature selection because of the evaluation function. Document frequency (DF) is one of several commonly methods used feature selection, its shortcomings is the lack of theoretical basis on function construction, itwill tend to select high-frequency words in selecting. To sol...

Özellik seçimi, veri analizinde hazırlamak için uygulanan ön işlemlerden biridir. seçimi basitçe orijinal özellik kümesinden en uygun özelliklerin alt kümesinin seçim işlemidir. Bu yöntemler, setinde alakasız ve gereksiz bilgiyi belirlemeye kaldırmaya çalışır. çalışmada sınıf bilgisi kullanılarak değişim katsayısına dayalı yeni bir yöntemi önerilmiştir. Önerilen yönteminin etkinliği, gerçek set...