A self-organizing incremental neural network for continual supervised learning. (15th December 2021)

• Record Type: Journal Article
• Authors: Wiwatcharakoses, Chayut; Berrar, Daniel
• Is Part Of: Expert systems with applications. Volume 185(2021)
• Publication Date: 2021-12-15
• Subjects: Catastrophic forgetting -- Concept drift -- Continual learning -- Incremental learning -- Supervised learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115662
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Continual learning algorithms can adapt to changes of data distributions, new classes, and even completely new tasks without catastrophically forgetting previously acquired knowledge. Here, we present a novel self-organizing incremental neural network, GSOINN+, for continual supervised learning. GSOINN+ learns a topological mapping of the input data to an undirected network and uses a weighted nearest-neighbor rule with fractional distance for classification. GSOINN+ learns incrementally—new classification tasks do not need to be specified a priori, and no rehearsal of previously learned tasks with stored training sets is required. In a series of sequential learning experiments, we show that GSOINN+ can mitigate catastrophic forgetting, even when completely new tasks are to be learned.