Clempner, Julio B. - ed. ; Ikonen, Enso - ed. ; Kurdyukov, Alexander P. - ed.
Title:Revisiting the optimal probability estimator from small samples for data mining
Subtitle: Group publication title: Subject and Keywords:probability estimation ; small samples ; minimal error ; m-estimate
Abstract:Estimation of probabilities from empirical data samples has drawn close attention in the scientific community and has been identified as a crucial phase in many machine learning and knowledge discovery research projects and applications. In addition to trivial and straightforward estimation with relative frequency, more elaborated probability estimation methods from small samples were proposed and applied in practice. ; In the context of an experimental framework, we present an in-depth analysis of several probability estimation methods with respect to their mean absolute errors and demonstrate their potential advantages and disadvantages. We extend the analysis from single instance samples to samples with a moderate number of instances. We define small samples for the purpose of estimating probabilities as samples containing either less than four successes or less than four failures and justify the definition by analysing probability estimation errors on various sample sizes.
Publisher:Zielona Góra: Uniwersytet Zielonogórski
Date: Resource Type: DOI: Pages: Source:AMCS, volume 29, number 4 (2019) ; click here to follow the link
Language: License CC BY 4.0: Rights: