Feature selection with redundancy-complementariness dispersion
نویسندگان
چکیده
منابع مشابه
Feature Selection with Redundancy-complementariness Dispersion
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two features are ignored. In this study, a modification item concerning the complemen...
متن کاملEfficient Spectral Feature Selection with Minimum Redundancy
Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features i...
متن کاملFast-Ensembles of Minimum Redundancy Feature Selection
Finding relevant subspaces in very highdimensional data is a challenging task not only for microarray data. The selection of features must be stable, but on the other hand learning performance is to be increased. Ensemble methods have succeeded in the increase of stability and classification accuracy, but their runtime prevents them from scaling up to real-world applications. We propose two met...
متن کاملFeature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Predictive models benefit from a compact, non-redundant subset of features that improves interpretability and generalization. Modern data sets are wide, dirty, mixed with both numerical and categorical predictors, and may contain interactive effects that require complex models. This is a challenge for filters, wrappers, and embedded feature selection methods. We describe details of an algorithm...
متن کاملFeature selection based on mutual information and redundancy-synergy coefficient.
Mutual information is an important information measure for feature subset. In this paper, a hashing mechanism is proposed to calculate the mutual information on the feature subset. Redundancy-synergy coefficient, a novel redundancy and synergy measure of features to express the class feature, is defined by mutual information. The information maximization rule was applied to derive the heuristic...
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: Knowledge-Based Systems
سال: 2015
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2015.07.004