Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …

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 …

In multi-label data, each instance corresponds to a set of labels instead of one label
whereby the instances belonging to a label in the corresponding column of that label are …

A commonly-encountered problem in foundation design is the reliable prediction of the pile
bearing capacity (PBC). This study is planned to propose a feasible soft computing …

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 …

In this paper, ensemble feature selection is modeled as a bi-objective optimization problem
regarding features' relevancy and redundancy degree. The proposed method, which is …

Now a day, all the organizations collecting huge volume of data without knowing its
usefulness. The fast development of Internet helps the organizations to capture data in many …