Add to ORKGThis paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi-label classification algorithm - in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the previously proposed Hill-Climbing for Multi-Label Correlation-Based Feature Selection (HC-ML-CFS), across 10 multi-label datasets
Multi-label classification (MLC) is the task of assigning multiple class labels to an object based o...
Multi-label classification is a fast-growing field of machine learning. Recent developments have sho...
Practical pattern classification and knowledge discovery problems require selection of a subset of a...
This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlatio...
The very large dimensionality of real world datasets is a challenging problem for classification alg...
In recent years, multi-label classification (MLC) has become an emerging research topic in big data ...
Abstract—In this paper we introduce a novel approach for classifier and feature selection in a multi...
© 2017 ACM. High-dimensional multi-labeled data contain instances, where each instance is associated...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
Multilabel feature selection involves the selection of relevant features from multilabeled datasets,...
As a commonly used technique in data preprocessing for machine learning, feature selection identifie...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
Feature Selection plays an important role in machine learning and data mining, and it is often appli...
Given a new dataset for classification in Machine Learning (ML), finding the best classification alg...
Multi-label classification (MLC) is the task of assigning multiple class labels to an object based o...
Multi-label classification is a fast-growing field of machine learning. Recent developments have sho...
Practical pattern classification and knowledge discovery problems require selection of a subset of a...
This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlatio...
The very large dimensionality of real world datasets is a challenging problem for classification alg...
In recent years, multi-label classification (MLC) has become an emerging research topic in big data ...
Abstract—In this paper we introduce a novel approach for classifier and feature selection in a multi...
© 2017 ACM. High-dimensional multi-labeled data contain instances, where each instance is associated...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
Multilabel feature selection involves the selection of relevant features from multilabeled datasets,...
As a commonly used technique in data preprocessing for machine learning, feature selection identifie...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
Feature Selection plays an important role in machine learning and data mining, and it is often appli...
Given a new dataset for classification in Machine Learning (ML), finding the best classification alg...
Multi-label classification (MLC) is the task of assigning multiple class labels to an object based o...
Multi-label classification is a fast-growing field of machine learning. Recent developments have sho...
Practical pattern classification and knowledge discovery problems require selection of a subset of a...