Feature selection for regression problems based on the Morisita estimator of intrinsic dimension
J Golay, M Leuenberger, M Kanevski - Pattern Recognition, 2017 - Elsevier
Data acquisition, storage and management have been improved, while the key factors of
many phenomena are not well known. Consequently, irrelevant and redundant features …
many phenomena are not well known. Consequently, irrelevant and redundant features …
Unsupervised feature selection based on the Morisita estimator of intrinsic dimension
J Golay, M Kanevski - Knowledge-Based Systems, 2017 - Elsevier
This paper deals with a new filter algorithm for selecting the smallest subset of features
carrying all the information content of a dataset (ie for removing redundant features). It is an …
carrying all the information content of a dataset (ie for removing redundant features). It is an …
[引用][C] Feature selection for regression problems based on the morisita estimator of intrinsic dimension: Concept and case studies
J Golay, M Leuenberger, MF Kanevski - CoRR, abs/1602.00216, 2016