Rough set is a tool with a mathematical foundation to deal with imprecise and imperfect
knowledge. It has been widely applied in machine learning, data mining and knowledge …

Objectives This study's objective was to examine whether commercial wearable devices
could accurately predict lying, sitting and varying intensities of walking and running …

We present coarse-to-fine autoregressive networks (C2FAR), a method for modeling the
probability distribution of univariate, numeric random variables. C2FAR generates a …

Consider a supervised dataset D=[A∣ b], where b is the outcome column, rows of D
correspond to observations, and columns of A are the features of the dataset. A central …

Recently, many researchers have studied in engineering approach to brain activity pattern of
conceptual activities of the brain. In this paper we proposed a intension recognition …

Feature selection is a process of selecting subset of features which are highly correlated
with classification outcome and lowly depends on other features. Rough set has been …

Feature selection and attribute reduction are crucial problems, and widely used techniques
in the field of machine learning, data mining and pattern recognition to overcome the well …

Feature selection is the process of sieving features, in which informative features are
separated from the redundant and irrelevant ones. This process plays an important role in …

Background: Closed-source software for processing and analyzing accelerometer data
provides little to no information about the algorithms used to transform acceleration data into …

A central problem in machine learning and pattern recognition is the process of recognizing
the most important features in a dataset. This process plays a decisive role in big data …