RT journal article
T1 Variable selection for Naive Bayes classification
A1 Blanquero, Rafael
A1 Carrizosa, Emilio
A1 Ramírez Cobo, Josefa
A1 Sillero-Denamiel, M. Remedios
A2 Estadística e Investigación Operativa
K1 Conditional independence
K1 Clustering
K1 Dependence measures
K1 Heuristics
K1 Probabilistic classification
K1 Cost-sensitive classification
AB The Naive Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naive Bayes' assumption of conditional independence, and may deteriorate the method's performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method's execution. In this paper we propose a sparse version of the Naive Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included. Our proposal leads to a smart search, which yields competitive running times, whereas the flexibility in terms of performance measure for classification is integrated. Our findings show that, when compared against well-referenced feature selection approaches, the proposed sparse Naive Bayes obtains competitive results regarding accuracy, sparsity and running times for balanced datasets. In the case of datasets with unbalanced (or with different importance) classes, a better compromise between classification rates for the different classes is achieved.
PB ELSEVIER
SN 0305-0548
YR 2021
FD 2021-11
LK http://hdl.handle.net/10498/25654
UL http://hdl.handle.net/10498/25654
LA eng
NO This research is partially supported by research grants and projects MTM2015-65915-R (Ministerio de Economia y Competitividad, Spain) and PID2019-110886RB-I00 (Ministerio de Ciencia, Innovacion y Universidades, Spain) , FQM-329 and P18-FR-2369 (Junta de Andalucia, Spain) , PR2019-029 (Universidad de Cadiz, Spain) , Fundacion BBVA and EC H2020 MSCA RISE NeEDS Project (Grant agreement ID: 822214) . This support is gratefully acknowledged.Document
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026