A Feature Selection Algorithm based on Mutual Information using Local Non-uniformity Correction Estimator
نویسندگان
چکیده
منابع مشابه
A new algorithm for EEG feature selection using mutual information
An EEG feature selection technique for the purpose of classification is developed. The technique selects those features that have maximum mutual information with the specified classes of interest (two classes in this case). Obviously, the simplest way is to consider all possible feature subsets (M out of N). However, even with a small number of features, this procedure is computationally imposs...
متن کاملA Novel Mutual Information-based Feature Selection Algorithm
From a machine learning point of view, identifying a subset of relevant features from a real data set can be useful to improve the results achieved by classification methods and to reduce their time and space complexity. To achieve this goal, feature selection methods are usually employed. These approaches assume that the data contains redundant or irrelevant attributes that can be eliminated. ...
متن کاملFeature Selection Based on Joint Mutual Information
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 ...
متن کاملFeature Selection Using Genetic Algorithm with Mutual Information
Feature selection is the problem of selecting a subset of features without reducing the accuracy of representing the original set of features. It is the most important step that affects the performance of a pattern recognition system. In this paper, genetic algorithm (GA) is used to implement a feature selection in filter based method, and the mutual information is served as a fitness function ...
متن کاملMutual Information-based multi-label feature selection using interaction information
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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Computer Science and Applications
سال: 2017
ISSN: 2156-5570,2158-107X
DOI: 10.14569/ijacsa.2017.080656