Tests for decision tables with many-valued decisions — Comparative study

Author

Azad, Mohammad ; Chikalov, Igor ; Moshkov, Mikhail ; Zielosko, B.

fYear

2012

fDate

9-12 Sept. 2012

Firstpage

271

Lastpage

277

Abstract

The paper is devoted to the study of a greedy algorithm for construction of approximate tests (super-reducts). This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. The idea of algorithm is connected with so-called boundary subtables. After constructing a test we use algorithm which tries to remove attributes from a test and obtain a reduct. We present experimental results connected with the cardinality of tests and reducts for randomly generated tables and data sets from UCI Machine Learning Repository which were converted to decision tables with many-valued decisions. To make some comparative study we presents also experimental results for greedy algorithm which constructs a test based on generalized decision approach.

Keywords

approximation theory; computational complexity; decision tables; greedy algorithms; learning (artificial intelligence); random processes; UCI machine learning repository; approximate test super-reduct construction; attribute removal; boundary subtables; decision table test cardinality; generalized decision approach; greedy algorithm; many-valued decisions; randomly generated table reducts; row labelling; Approximation algorithms; Computer science; Educational institutions; Greedy algorithms; Machine learning; Machine learning algorithms; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on

Conference_Location

Wroclaw

Print_ISBN

978-1-4673-0708-6

Electronic_ISBN

978-83-60810-51-4

Type

conf

Filename

6354321