Du, Zhi-GangPan, Tien-SzuPan, Jeng-ShyangChu, Shu-ChuanQUasi-Affine TRansformation Evolutionary Algorithm (QUATRE) is a currently emerging meta-heuristic evolutionary algorithm. QUATRE has the ability to balance exploitation and exploration in the optimization process, and the algorithm optimization uses matrix operations to greatly reduce the time complexity for solving the same problem. This series of advantages makes this algorithm adopted by a large number of researchers. In this paper, QUATRE is used to optimize the Feature Selection (FS) of the wrapper method. K-Fold Cross-Validation (KFCV) method is also used to divide the test set and training set of the sample, and then use the K Nearest Neighbor (KNN) algorithm for feature classification. In the optimization process, we use a threshold (choice) for feature identification to select useful features. Finally, the 9 standard test data sets in UCI are used to verify the effectiveness of the QUATRE algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    QUasi-Affine TRansformation Evolutionary Algorithm for Feature Selection


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Wu, Tsu-Yang (Herausgeber:in) / Ni, Shaoquan (Herausgeber:in) / Chu, Shu-Chuan (Herausgeber:in) / Chen, Chi-Hua (Herausgeber:in) / Favorskaya, Margarita (Herausgeber:in) / Du, Zhi-Gang (Autor:in) / Pan, Tien-Szu (Autor:in) / Pan, Jeng-Shyang (Autor:in) / Chu, Shu-Chuan (Autor:in)


    Erscheinungsdatum :

    2021-11-30


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    QUasi-Affine TRansformation Evolutionary Algorithm for Feature Selection

    Du, Zhi-Gang / Pan, Tien-Szu / Pan, Jeng-Shyang et al. | TIBKAT | 2022




    An QUasi-Affine TRansformation Evolution (QUATRE) Algorithm for Job-Shop Scheduling Problem by Mixing Different Strategies

    Yang, Qing-Yong / Chu, Shu-Chuan / Chen, Chien-Ming et al. | Springer Verlag | 2021