Feature selection using Joint Mutual Information Maximisation
نویسندگان
چکیده
منابع مشابه
Feature selection using Joint Mutual Information Maximisation
Feature selection is used in many application areas relevant to expert and intelligent systems, such as data mining and machine learning, image processing, anomaly detection, bioinformatics and natural language processing. Feature selection based on information theory is a popular approach due its computational efficiency, scalability in terms of the dataset dimensionality, and independence fro...
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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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In this paper, we propose a hybrid filter/wrapper approach for fast feature selection using the Residual Mutual Information (RMI) between the function approximator output and the remaining features as selection criterion. This approach can handle redundancies in the data as well as the bias of the employed learning machine while keeping the number of required training and evaluation procedures ...
متن کاملQuadratic Mutual Information Feature Selection
We propose a novel feature selection method based on quadratic mutual information which has its roots in Cauchy–Schwarz divergence and Renyi entropy. The method uses the direct estimation of quadratic mutual information from data samples using Gaussian kernel functions, and can detect second order non-linear relations. Its main advantages are: (i) unified analysis of discrete and continuous dat...
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Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than singlelabel feature selection tasks. In this paper, we propose the Mutual Information-based m...
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ژورنال
عنوان ژورنال: Expert Systems with Applications
سال: 2015
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2015.07.007