Abstract Sensitivity analysis is a popular feature selection approach employed to identify the important features in dataset. In sensitivity analysis, each input perturbed one-at-a-time and response of machine learning model examined determine feature's rank. Note that existing perturbation techniques may lead inaccurate ranking due their parameters. This study proposes novel involves using com...

In the field of computer vision, detection multiple objects with different scales within a single image is challenging. To target this problem, feature pyramids are basic component commonly found in multi-scale object detectors. construction standard pyramids, semantic features simply connected to rebuild new map, regardless whether these have positive effect output or not. order avoid introduc...

This letter presents an unsupervised feature selection method based on machine learning. Feature is important component of artificial intelligence, learning, which can effectively solve the curse dimensionality problem. Since most labeled data expensive to obtain, this paper focuses method. The distance metric traditional algorithms usually Euclidean distance, and it maybe unreasonable map high...

This paper introduces a new technique for feature selection and illustrates it on a real data set. Namely, the proposed approach creates subsets of attributes based on two criteria: (1) individual attributes have high discrimination (classification) power; and (2) the attributes in the subset are complementary that is, they misclassify different classes. The method uses information from a confu...

Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle a...

Analysis of high dimensional noisy data is of essence across a variety of research fields. Feature selection techniques are designed to find the relevant feature subset that can facilitate classification or pattern detection. Traditional (supervised) feature selection methods utilize label information to guide the identification of relevant feature subsets. In this paper, however, we consider t...

Recursive Feature Elimination (RFE) combined with feature ranking is an effective technique for eliminating irrelevant features when the feature dimension is large, but it is difficult to distinguish between relevant and redundant features. The usual method of determining when to stop eliminating features is based on either a validation set or cross-validation techniques. In this paper, we pres...