Abstract Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Presented in this article are semi-supervised artificial neural network- (ANN) and support vector machine- (SVM) based classifiers designed by the self-configuring genetic algorithm (SelfCGA) and the fuzzy controlled meta-heuristic approach Co-operation of Biology Related Algorithms (COBRA). Both data mining tools are based on dividing instances from different classes using both labelled and unlabelled examples. A new collective bionic algorithm, namely fuzzy controlled cooperation of biology-related algorithms, which solves constrained optimization problems, COBRA-cf, has been developed for the design of semi-supervised SVMs. Firstly, the experimental results obtained by the two types of fuzzy controlled COBRA are presented and compared and their usefulness is demonstrated. Then the performance and behaviour of the proposed semi-supervised SVMs and semi-supervised ANNs were studied under common experimental settings and their workability was established. Then their efficiency was estimated on a speech-based emotion recognition problem. Thus, the workability of the proposed meta-heuristic optimization algorithms was confirmed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Semi-supervised Data Mining Tool Design with Self-tuning Optimization Techniques


    Beteiligte:


    Erscheinungsdatum :

    2019-04-18


    Format / Umfang :

    19 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Trajectory data mining method based on deep semi-supervised neural network

    ZHANG DENGYIN / YANG XIAORUN / DING FEI et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Semi-Supervised Self-Training of Object Detection Models

    Rosenberg, Chuck / Hebert, Martial / Schneiderman, Henry | IEEE | 2005



    Semi-Supervised Face Detection

    Sebe, N. / Cohen, I. / Huang, T.S. et al. | IEEE | 2005


    Data Mining for Evolutionary Design Optimization

    Lian, Yongsheng / Liou, Meng-Sing | AIAA | 2004