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Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm
Since different features may require different costs, the cost-sensitive feature selection
problem become more and more important in real-world applications. Generally, it includes …
problem become more and more important in real-world applications. Generally, it includes …
Rough set based feature selection: a review
Rough set is a tool with a mathematical foundation to deal with imprecise and imperfect
knowledge. It has been widely applied in machine learning, data mining and knowledge …
knowledge. It has been widely applied in machine learning, data mining and knowledge …
Mutual information criterion for feature selection from incomplete data
W Qian, W Shu - Neurocomputing, 2015 - Elsevier
Feature selection is an important preprocessing step in machine learning and data mining,
and feature criterion arises a key issue in the construction of feature selection algorithms …
and feature criterion arises a key issue in the construction of feature selection algorithms …
Finding rough and fuzzy-rough set reducts with SAT
Feature selection refers to the problem of selecting those input features that are most
predictive of a given outcome; a problem encountered in many areas such as machine …
predictive of a given outcome; a problem encountered in many areas such as machine …
Eco-efficiency assessment of water systems in China
Y Liu, C Sun, S Xu - Water resources management, 2013 - Springer
With the transformation of water conservancy from traditional to eco-hydraulic aiming at
sustainable development, the study on eco-efficiency of the water system has attracted a …
sustainable development, the study on eco-efficiency of the water system has attracted a …
A dual-branch network for few-shot vehicle re-identification with enhanced global and local features
Traditional vehicle re-identification (Re-ID) methods mainly rely on large-size training
samples to achieve better results. However, obtaining abundant training samples is …
samples to achieve better results. However, obtaining abundant training samples is …
Multi-criteria feature selection on cost-sensitive data with missing values
Feature selection plays an important role in pattern recognition and machine learning.
Confronted with high dimensional data in many data analysis tasks, feature selection …
Confronted with high dimensional data in many data analysis tasks, feature selection …
Semi_Fisher Score: A semi-supervised method for feature selection
Feature selection is an important problem for pattern classifier systems. As compared to
unsupervised feature selection methods, supervised feature selection approaches have …
unsupervised feature selection methods, supervised feature selection approaches have …
Minimal attribute reduction with rough set based on compactness discernibility information tree
Y Jiang, Y Yu - Soft Computing, 2016 - Springer
Minimal attribute reduction plays an important role in rough set. Heuristic algorithms are
proposed in literature reviews to get a minimal reduction and yet an unresolved issue is that …
proposed in literature reviews to get a minimal reduction and yet an unresolved issue is that …
Eigen-level data fusion model by integrating rough set and probabilistic neural network for structural damage detection
In this paper, a new eigen-level data fusion model, whereby rough set data and a
probabilistic neural network (PNN) are integrated using a data fusion technique, is proposed …
probabilistic neural network (PNN) are integrated using a data fusion technique, is proposed …