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Relative reduct-based estimation of relevance for stylometric features

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In rough set theory characteristic features, which describe classified objects, correspond to conditional attributes. A relative reduct is such an irreducible subset of attributes that preserves the quality of approximation of a complete decision table. For a decision table a single reduct or many reducts may exist. In typical processing one reduct is selected for the subsequent generation of decision rules, while others can be discarded. Yet when the set of reducts is analysed as a whole, observations and conclusions drawn can be used to evaluate relevance of attributes, which in turn can be employed in reduction of features not only for rule-based but also connectionist classifiers. The paper describes the steps of such procedure applied in the domain of stylometric processing of literary texts.

Original languageEnglish
Title of host publicationAdvances in Databases and Information Systems - 17th East European Conference, ADBIS 2013, Proceedings
Pages135-147
Number of pages13
DOIs
Publication statusPublished - 2013
Event17th East-European Conference on Advances in Databases and Information Systems, ADBIS 2013 - Genoa, Italy
Duration: 1 Sept 20134 Sept 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8133 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th East-European Conference on Advances in Databases and Information Systems, ADBIS 2013
Country/TerritoryItaly
CityGenoa
Period1/09/134/09/13

Keywords

  • ANN
  • Characteristic Feature
  • DRSA
  • Decision Algorithm
  • Relative Reduct
  • Relevance Measure
  • Stylometry

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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