The short-term load forecasting is an essential problem in energy system planning and operation. The accuracy of the forecasting models depends on the quality of the input information. The input variable selection allows to chose the most informative inputs which ensure the best forecasts. To improve the short-term load forecasting model based on the kernel regression four variable selection wr...

The aim of the present paper is to develop efficient feature selection approaches. A novel wrapper methodology for feature selection is formulated based on the Fish School Search (FSS) optimization algorithm, intended to cope with premature convergence. In order to use this population based optimization algorithm in feature selection problems, the use of binary encoding for the internal mechani...

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...

Abstract Aiming at the characteristics of a long short-term memory network (LSTM) which is suitable for processing high-dimensional, strongly coupled, and highly time-dependent data, it combines advantages feature selection to reduce difficulty learning tasks improve performance model fault diagnosis. This paper proposes an LSTM method combining sequential floating forward search with integrate...

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...