AN APPLICATION OF FEATURE SELECTION FOR THE FUZZY RULE BASED CLASSIFIER DESIGN BASED ON AN ENLARGED HEDGE ALGEBRAS FOR HIGH-DIMENSIONAL DATASETS
Keywords:Hedge Algebras, Fuzzy Classification System, Feature Selection, High-dimensional Dataset
AbstractThe fuzzy rule based classification system (FRBCS) design methods, whose fuzzy rules are in the form of if-then sentences, have been being studied intensively during last years. One of the eminent FRBCS design methods utilizing an enlarged hedge algebras as a formal mechanism to design optimal linguistic terms integrated with their trapezoidal fuzzy sets has been proposed in . As the other methods, a difficult problem needed to be solved is dealing with the high-dimensional and multi-instance datasets. This paper presents an approach to tackle the high-dimensional dataset problem for the FRBCS design method based on an enlarged hedge algebras proposed in  by utilizing the feature selection algorithm proposed in . The experimental results over 8 high-dimensional datasets have shown that the proposed method allows saving much execution time than the original one, but retains the equivalent classification performance as well as the equivalent FRBCS complexity.
Authors who publish with Vietnam Journal of Science and Technology agree with the following terms:
- The manuscript is not under consideration for publication elsewhere. When a manuscript is accepted for publication, the author agrees to automatic transfer of the copyright to the editorial office.
- The manuscript should not be published elsewhere in any language without the consent of the copyright holders. Authors have the right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of their work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are encouraged to post their work online (e.g., in institutional repositories or on their websites) prior to or during the submission process, as it can lead to productive exchanges or/and greater number of citation to the to-be-published work (See The Effect of Open Access).