Bayesian approach to incremental batch learning on forest cover sensor data for multiclass classification. (December 2021)

• Record Type: Journal Article
• Authors: D, Venkata Vara Prasad; Venkataramana, Lokeswari Y; S, Saraswathi; Mathew, Sarah; V, Snigdha
• Is Part Of: Concurrent engineering, research and applications. Volume 29:Number 4(2021)
• Page Start: 405
• Page End: 414
• Publication Date: 2021-12
• Subjects: Bayesian incremental learning -- Crème incremental learning -- multiclass classification -- Gaussian and scale mixer priors -- Google Co laboratory; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X211058450
• Languages: English
• ISSNs: 1063-293X

Abstract

Deep neural networks can be used to perform nonlinear operations at multiple levels, such as a neural network that is composed of many hidden layers. Although deep learning approaches show good results, they have a drawback called catastrophic forgetting, which is a reduction in performance when a new class is added. Incremental learning is a learning method where existing knowledge should be retained even when new data is acquired. It involves learning with multiple batches of training data and the newer learning sessions do not require the data used in the previous iterations. The Bayesian approach to incremental learning uses the concept of the probability distribution of weights. The key idea of Bayes theorem is to find an updated distribution of weights and biases. In the Bayesian framework, the beliefs can be updated iteratively as the new data comes in. Bayesian framework allows to update the beliefs iteratively in real-time as data comes in. The Bayesian model for incremental learning showed an accuracy of 82%. The execution time for the Bayesian model was lesser on GPU (670 s) when compared to CPU (1165 s).