Name
Abstract | ||
|---|---|---|
Computational models of the artificial intelligence such as soft set theory have several applications. Soft data reduction can be considered as a machine learning technique for features selection. In this paper, we present the applicability of soft set theory for feature selection of Traditional Malay musical instrument sounds. The modeling processes consist of three stages: feature extraction, data discretization and finally using the multi-soft sets approach for feature selection through dimensionality reduction in multivalued domain. The result shows that the obtained features of proposed model are 35 out of 37 attributes. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16167-4_33 | ICICA (LNCS) |
Keywords | Field | DocType |
feature selection,artificial intelligence,feature extraction,soft data reduction,traditional malay musical instrument,soft set theory,features selection,computational model,data discretization,dimensionality reduction,computer model,data reduction,machine learning,artificial intelligent,set theory | Discretization,Dimensionality reduction,Pattern recognition,Feature selection,Feature (computer vision),Computer science,Soft set,Musical instrument,Feature extraction,Computational model,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
6377 | 0302-9743 | 3-642-16166-9 |
Citations | PageRank | References |
4 | 0.41 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Norhalina Senan | 1 | 11 | 4.01 |
| Rosziati Ibrahim | 2 | 46 | 13.87 |
| Nazri Mohd Nawi | 3 | 158 | 22.90 |
| Iwan Tri Riyadi Yanto | 4 | 64 | 7.29 |
| Tutut Herawan | 5 | 608 | 75.21 |