In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

Multilabel data contains rich label semantic information, and its data structure conforms to
the cognitive laws of the actual world. However, these data usually involve many irrelevant …

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 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 …

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 …

The performance of multilabel learning depends heavily on the quality of the input features.
A mass of irrelevant and redundant features may seriously affect the performance of …

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 …

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 …

Today, many metaheuristics algorithms have been developed are inspired by the physical
phenomena or behaviors of natural creatures that are very effective in solving complex …