Feature selection based on weighted conditional mutual information
نویسندگان
چکیده
منابع مشابه
Conditional Dynamic Mutual Information-Based Feature Selection
With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in th...
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In a context of classi cation, we propose to use conditional mutual information to select a family of binary features which are individually discriminating and weakly dependent. We show that on a task of image classi cation, despite its simplicity, a naive Bayesian classi er based on features selected with this Conditional Mutual Information Maximization (CMIM) criterion performs as well as a c...
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In this paper, we apply weighted Mutual Information for effective feature selection. The presented hybrid filter wrapper approach resembles the well known AdaBoost algorithm by focusing on those samples that are not classified or approximated correctly using the selected features. Redundancies and bias of the employed learning machine are handled implicitly by our approach. In experiments, we c...
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We propose a sequential forward feature selection method to find a subset of features that are most relevant to the classification task. Our approach uses novel estimation of the conditional mutual information between candidate feature and classes, given a subset of already selected features which is utilized as a classifier independent criterion for evaluation of feature subsets. The proposed ...
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A feature/input selection method is proposed based on joint mutual information. The new method is better than the existing methods based on mutual information in eliminating redundancy in the inputs. It is applied in a real world application to nd 2-D viewing coordinates for data visualization and to select inputs for a neural network classiier. The result shows that the new method can nd many ...
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ژورنال
عنوان ژورنال: Applied Computing and Informatics
سال: 2020
ISSN: 2634-1964,2210-8327
DOI: 10.1016/j.aci.2019.12.003