Feature selection is one of the most efficient procedures for reducing the dimensionality of
high-dimensional data by choosing a practical subset of features. Since labeled samples are …

In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria
decision making (MCDM) process. This method is applied to a multi-label data and we have …

For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-
Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a …

In recent years, multi-label learning becomes a trending topic in machine learning and data
mining. This type of learning deals with data that each instance is associated with more than …

Abstract Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic
algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired …

Multi-label learning algorithms have significant challenges due to high-dimensional feature
space and noises in multi-label datasets. Feature selection methods are effective techniques …

The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing
this type of retaining structures. On the other hand, the feasibility of artificial intelligence …

With the increase of the number of features and the sample size, existing feature selection
(FS) methods based on evolutionary optimization still face challenges such as the “curse of …

This paper proposed a Multi-Criteria Decision-Making (MCDM) modeling to deal with multi-
target regression problem. This model offered a feature ranking approach for multi-target …

Abstract Wireless Sensor Networks (WSNs) are the main component in the Internet of Things
(IoT) and smart cities to sense our environment, gather essential and meaningful data, and …