<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Attribute reduction is a key issue in the research of rough sets. Aiming at the shortcoming of attribute reduction algorithm based on discernibility matrix, an attribute reduction method based on sample extraction and priority is presented. Firstly, equivalence classes are divided using quick sort for computing compressed decision table. Secondly, important samples are extracted from compressed decision table using iterative self-organizing data analysis technique algorithm(ISODATA). Finally, attribute reduction of sample decision table is conducted based on the concept of priority. Experimental results show that the attribute reduction method based on sample extraction and priority can significantly reduce the overall execution time and improve the reduction efficiency.</p>
</abstract>

Attribute reduction is a key issue in the research of rough sets. Aiming at the shortcoming of attribute reduction algorithm based on discernibility matrix, an attribute reduction method based on sample extraction and priority is presented. Firstly, equivalence classes are divided using quick sort for computing compressed decision table. Secondly, important samples are extracted from compressed decision table using iterative self-organizing data analysis technique algorithm(ISODATA). Finally, attribute reduction of sample decision table is conducted based on the concept of priority. Experimental results show that the attribute reduction method based on sample extraction and priority can significantly reduce the overall execution time and improve the reduction efficiency.

Attribute Reduction Method Based on Sample Extraction and Priority

Endicott College of International Studies

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Attribute Reduction Method Based on Sample Extraction and Priority"}

Attribute reduction is a key issue in the research of rough sets. Aiming at the shortcoming of attribute reduction algorithm based on discernibility matrix, an...

Data sets	\|U\|	\|C\|
Statlog	6435	36
Winequality	4898	11
Contraceptive	1473	9

U	a	b	c	D
u₁	1	0	1	0
u₂	2	0	1	1
u₃	1	0	1	0
u₄	0	0	0	0
u₅	1	2	0	1
u₆	1	2	0	1
u₇	2	2	0	1

Sampling distance	Sample number	Attribute number of minimum reduction	Computing time (s)
0.6D_i	5473	9	329.61
0.8D_i	3965	8	195.13
D_i	2648	8	111.42
1.05D_i	2344	7	79.82
1.1D_i	2077	7	65.15
1.2D_i	1628	6	54.59

U	a	b	c	D
u₂	2	0	1	1
u₃	1	0	1	0
u₄	0	0	0	0
u₅	1	2	0	1
u₇	2	2	0	1

\|U\|			Traditional algorithm		Algorithm 4
Original data set	Redundancy removal	Sampling	Reduction result	Reduction time (s)	Reduction result	Computing time (s)
1473	1425	665	9	12.62	8	4.13

Attribute Reduction Method Based on Sample Extraction and Priority

Online veröffentlicht: 12. Apr. 2021

Seitenbereich: 219 - 226

Eingereicht: 28. Nov. 2020

Akzeptiert: 31. Jan. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00036

Schlüsselwörter
rough sets, sample extraction, priority, attribute reduction, algorithm

© 2021 Biqing Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

UCI data sets.

A redundant decision table.

The relationship between different sampling distances and reduction effectiveness for Statlog.

Compressed decision table.

Comparison on the effect of reduction for Contraceptive data set.

Comparison on the effect of reduction for Winequality data set

Attribute Reduction Method Based on Sample Extraction and Priority

Online veröffentlicht: 12. Apr. 2021

Seitenbereich: 219 - 226

Eingereicht: 28. Nov. 2020

Akzeptiert: 31. Jan. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00036

Schlüsselwörterrough sets, sample extraction, priority, attribute reduction, algorithm

© 2021 Biqing Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

UCI data sets.

A redundant decision table.

The relationship between different sampling distances and reduction effectiveness for Statlog.

Compressed decision table.

Comparison on the effect of reduction for Contraceptive data set.

Comparison on the effect of reduction for Winequality data set

Schlüsselwörter
rough sets, sample extraction, priority, attribute reduction, algorithm