ACADSTAFF UGM
| Title | : | Vectorization Method Based on High Correlation for Multivariate Time Series Hybrid Filter Wrapper Feature Selection |
| Author | : |
RR ANI DIJAH RAHAJOE (1) Drs. Edi Winarko, M.Sc.,Ph.D. (2) Prof. Drs. Suryo Guritno, M.Stats., Ph.D. (3) |
| Date | : | 0 2023 |
| Keyword | : | Vectorization, Support vector machine, Wrapper, Filter, Genetic algorithm Vectorization, Support vector machine, Wrapper, Filter, Genetic algorithm |
| Abstract | : | One of the techniques to reduce the multivariate time series dimension is to transform each Multivariate Time Series (MTS) dataset into a single row or column called vectorization. This paper contributes to using a new method in forming vectorization based on principal component analysis through an observation time analysis factor of each multivariate time series data without removing any information from the original data. The vectorization method is called Vectorization for Time of Observation Based on High Correlation (VecTOR), which is included in the filter method for feature selection. The wrapper method selects variables from the vectorization matrix with the Genetic Algorithm – Support Vector Machine algorithm (GASVM). VecTOR-GASVM is compared to four other methods: VecTOR – Support Vector Machine (VecTOR-SVM), VecTORGABayes, VecTOR Forward-Bayes, and VecTOR Backward-Bayes. The proposed method has been tested on the CMU and Wafer datasets. Results have shown that the feature selection of hybrid filter wrapper VecTOR has fewer features with the highest accuracy compared to the other four methods. In CMU data, the VecTOR-GASVM method has an accuracy of 100 per cent with 11 features selected. For the Wafer set of data, VecTORGASVM has an accuracy of 97.98 per cent with 2 features selected. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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