Many feature selection and feature ranking methods have been proposed. Using real and artificial data an attempt has been made to compare some of these methods. The "feature relevance index" used seems to have little effect on the relative ranking. For continuous features discretization and kernel smoothing are compared. Selection of subsets of features using hashing techniques is compared with...

Many indices for evaluation of features have been considered. Applied to single features they allow for filtering irrelevant attributes. Algorithms for selection of subsets of features also remove redundant features. Hashing techniques enable efficient application of feature relevance indices to selection of feature subsets. A number of such methods have been applied to artificial and real-worl...

The objective of this article is to develop an empirically based framework for formulating and selecting a vendor in supply chain. This study applies the fuzzy set theory to evaluate the vendor selection decision. Applying Analytic Hierarchy Process (AHP) in obtaining criteria weights and applied Technique for Order Performance by Similarity to Idea Solution (TOPSIS) for obtaining final ranking...

Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called "hybrid approach", using incorporation of Multi Resolution Analysis...

The abundance of algorithms developed to solve different problems has given rise to an important research question: How do we choose the best algorithm for a given problem? Known as algorithm selection, this issue has been prevailing in many domains, as no single algorithm can perform best on all problem instances. Traditional algorithm selection and portfolio construction methods typically tre...

Many ranking-and-selection (R&S) procedures have been invented for choosing the best simulated system; in this paper we consider indifference-zone procedures that attempt to provide a probability of correct selection (PCS) guarantee. To obtain the PCS guarantee, existing procedures nearly always exploit knowledge about the particular combination of system performance measure (e.g., mean, probab...

We describe in this paper our system for the Prior-art task of CLEFIP 2010 (a task focused on the retrieval of relevant patents to a given one) and its results. We have developed a system where patents are indexed by fields in order to allow a selection of the most discriminative terms of each field, applying Kullback-Leibler divergence as feature selection method, and using different boost fac...

In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of cues. Mapping the problem on an affinity graph...

this paper applies a new multi attribute decision-making (madm) model to help companies for enterprise resource planning (erp) selection problem based on balanced score card method. this paper uses three-parameter interval grey numbers which is derived from grey theory (was proposed by j. deng). this numbers is used instead of linguistic variables. beside, a new weighting method that outcomes f...