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Incremental learning refers to the learning of new information iteratively without having to fully retain the classifier. However, a single classifier cannot realize incremental learning if the classification problem is too complex and scalable. To solve the problem, this paper combines the incremental support vector machine (ISVM) and the incremental neural network Learn++ into a novel incremental learning algorithm called the ISVM-Learn++. The two incremental classifiers were merged by parallel combination and weighted sum combination. The proposed algorithm was tested on three datasets, namely, three databases Ionosphere, Haberman's Survival, and Blood Transfusion Service Center. The results show that the ISVM-Learn ++ achieved a learning rate of 98 %, better than that of traditional incremental learning algorithms. The research findings shed new light on incremental supervised machine learning.

Item Type	Journal
Divisions	» Faculty of Science and Technology
ePrint ID	3719
Date Deposited	2022-12-14
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/3719

@article{uniusa3719,
    title={A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++},
    author={Aida CHEFROUR, SOUICI-MESLATI Labiba, Iness Difi and Nesrine Bakkouche},
    journal={Revue d'Intelligence Artificielle}
    year={2019},
    volume={33},
    number={3},
    pages={181-188},
    publisher={Lavoisier}
}