Recently the research on supervised term weighting has attracted growing attention in the field of Traditional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show that existing methods more or less suffer from the problem of over-weighting. Overlooked by prior studies, over-weighting is a new concept proposed in this paper. To address this probl...

We present an improved method for calculating the parallel and perpendicular velocity correlation functions directly from peculiar surveys using weighted maximum-likelihood estimators. A central feature of new is use a position-dependent weighting scheme that reduces influence nearby galaxies, which are typically overrepresented relative to more distant galaxies in most surveys. demonstrate cal...

the aim of this study preparation of the predicted soil maps by using kriging and inverse distance weighting methods in east rangeland of semnan. sampling was done within each unit of sampling parallel transects and 1 vertical transect with 750m length. soil samples were taken from 0-20 cm in starting and ending points of each transect. there were used kriging and inverse distance weighting met...

Class imbalance problem in data, plays a critical role in use of machine learning methods for text classification since feature selection methods expect homogeneous distribution as well as machine learning methods. This study investigates two different kinds of feature selection metrics (one-sided and two-sided) as a global component of term weighting schemes (called as tffs) in scenarios where...

Text Categorization is the process of automatically assigning predefined categories to free text documents. Feature weighting, which calculates feature (term) values in documents, is one of important preprocessing techniques in text categorization. This paper is a comparative study of feature weighting methods in statistical learning of Thai Document Categorization Framework. Six methods were e...

This paper investigates a method of reconstructing images from energy-resolved CT data with negligible beam-hardening artifacts and improved contrast-to-nosie ratio (CNR) compared to conventional energy-weighting methods. Conceptually, the investigated method first reconstructs separate images from each energy bin. The final image is a linear combination of the energy-bin images, with the weigh...