Abstract
Probabilistic classifiers output a probability of an input being a member of each of the possible classes, given some of its feature values, selecting most probable class as predicted class. We introduce and compare different measures of the feature strength in probabilistic confidence-weigthed classification models. For that, we follow two approaches: one based on conditional probability tables of the classification variable with respect to each feature, using different statistical distances and a correction parameter, and the second one based on accuracy in predicting classification from evidences on each isolated feature. On a case study, we compute these feature strength measures and rank features attending to them, comparing results.
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Acknowledgments
The authors are supported by Ministerio de Economía y Competitividad, Gobierno de España, project ref. MTM2015 67802-P, and belong to the “Quantitative Methods in Criminology” research group of the Universitat Autònoma de Barcelona. They wish to express their acknowledgment to the Secretary of State for Security and the Prosecution Office of Environment and Urbanism of the Spanish state, for providing dataset used in the case study.
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Appendix: Conditional Probability Tables of Features with Respect to Class Variable \(A_{15}\)
Appendix: Conditional Probability Tables of Features with Respect to Class Variable \(A_{15}\)
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Delgado, R., Tibau, XA. (2018). Measuring Features Strength in Probabilistic Classification. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_31
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DOI: https://doi.org/10.1007/978-3-319-91473-2_31
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