Zobrazeno 1 - 10
of 454
pro vyhledávání: '"Minimum redundancy feature selection"'
Autor:
Perumal K, Mohana Chelvan P
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in informa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39c52e6488c1f4a6586e29c4373f3063
Publikováno v:
Neural Processing Letters. 52:1339-1358
Feature selection is one of the major aspects of pattern classification systems. In previous studies, Ding and Peng recognized the importance of feature selection and proposed a minimum redundancy feature selection method to minimize redundant featur
A maximum relevancy and minimum redundancy feature selection approach for median filtering forensics
Autor:
Aanchal Agarwal, Abhinav Gupta
Publikováno v:
Multimedia Tools and Applications. 79:21743-21770
The forensics of the median filtering is a challenging task due to its content preserving nature. Several methods have been proposed for median filtering forensics in digital images. However the performance of these methods deteriorates for compresse
Autor:
Zhe Ju, Shi-Yun Wang
Publikováno v:
Chemometrics and Intelligent Laboratory Systems. 191:96-102
As a new type of histone mark, lysine 2-Hydroxyisobutyrylation (Khib) is known to affect the association between histone and DNA. The accurate identification of Khib sites is significant for further exploration of the biological functions and molecul
Autor:
Waheeda Almayyan
Parkinson’s disease is a complex chronic neurodegenerative disorder of the central nervous system. One of the common symptoms for the Parkinson’s disease subjects, is vocal performance degradation. Patients usually advised to follow personalized
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9693517aca46a996d670ff9dc2bef0e6
Publikováno v:
Pattern Recognition Letters. 109:89-96
Previous Spectral Feature Selection (SFS) methods output promising feature selection results in many real-world applications, which deeply depend on the preservation of the local or global structures of the data via learning a graph matrix. However,
Publikováno v:
IEEE Transactions on Fuzzy Systems. 26:734-748
Features that have good predictive power for classes or output variables are useful features and hence most feature selection methods try to find them. However, since there may be high correlation or nonlinear dependence between such good features, w
Publikováno v:
Neurocomputing. 275:2824-2830
In this paper, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) method is firstly proposed, such that optimal weight can be automatically achieved to balance both between-class and within-class scatter matrices. Since correlated
Publikováno v:
Information Processing Letters. 129:44-52
Feature selection is a popular data pre-processing step. The aim is to remove some of the features in a data set with minimum information loss, leading to a number of benefits including faster running time and easier data visualisation. In this paper
Autor:
Yong Fan, Hanyang Peng
Publikováno v:
Information Sciences. :652-667
A unified framework is proposed to select features by optimizing computationally feasible approximations of high-dimensional conditional mutual information (CMI) between features and their associated class label under different assumptions. Under thi