Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …

High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Crude oil price forecasting has attracted much attention due to its significance on
commodities market as well as nonlinear complexity in prediction task. Combining forecasts …

Risk assessment is a crucial element in the life insurance business to classify the applicants.
Companies perform underwriting process to make decisions on applications and to price …

Gene selection is the process of selecting the optimal feature subset in an arbitrary dataset.
The significance of gene selection is in high dimensional datasets in which the number of …

High-dimensional data in many machine learning applications leads to computational and
analytical complexities. Feature selection provides an effective way for solving these …

The applications of traditional statistical feature selection methods to high-dimension, low-
sample-size data often struggle and encounter challenging problems, such as overfitting …

Conventional feature selection algorithms select a global feature subset for the entire
sample space. In contrast, in this paper we propose an efficient filter local feature selection …

Among several indicators for river engineering sustainability, the longitudinal dispersion
coefficient (K x) is the main parameter that defines the transport of pollutants in natural …

The classification accuracy of a multi-layer perceptron (MLP) depends on the selection of
relevant features from the data set, its architecture, connection weights and the transfer …