Characterization of classification algorithms

Lecture notes in computer sciencePublished 1 January 1995

João Gama, Pavel Brazdil

DOI10.1007/3-540-60428-6_16

Source URL

Citations117

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

This paper is concerned with the problem of characterization of classification algorithms and involves generation of different kinds of models, which include regression and rule models, piecewise linear models (model trees) and instance based models.

Abstract

This paper is concerned with the problem of characterization of classification algorithms. The aim is to determine under what circumstances a particular classification algorithm is applicable. The method used involves generation of different kinds of models. These include regression and rule models, piecewise linear models (model trees) and instance based models. These are generated automatically on the basis of dataset characteristics and given test results. The lack of data is compensated for by various types of preprocessing. The models obtained are characterized by quantifying their predictive capability and the best models are identified.

Keywords

Computer Science

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