Adaptive Online Sequential ELM for Concept Drift Tackling

Abstract

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition.

Accuracy Performance Degradation in Image Classification Models due to Concept Drift

“…This research study examines the multiple Concept Drift scenario (Real, Virtual and Hybrid) and its behavior with several Shallow Learning (SVM, ELM, OSELM) and Deep Learning models (CNN, ResNet-50) in online image stream scenarios. MNIST and CIFAR 10 are considered as benchmark datasets [6] for grey scale (2 channels) and RGB (3 channels) images [7]. In this study we carried out two different experiments to understand the behavior of Machine Learning models due to Concept Drift.…”

“…In Machine Learning, MNIST is recognized as the benchmark dataset for greyscale images [6][7] [8]. The MNIST dataset contained 70,000 handwritten images (28x28 pixel) with 10 target classes [9].…”

A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models

“…Dynamic assumptions of data (features of data changes over time) called Concept Drift [2]. The Concept Drift term in Machine Learning (ML) is being recognized as the most critical problem since many decades for traditional data and big data.…”

“…www.ijacsa.thesai.org Furthermore, if the P (X) (feature-wise distribution of data changes) due to insufficient or partial feature representation of existing data distribution (new additional feature adds or some feature updates) called as Virtual Drift [5]. Also, a study introduces Hybrid Drift as a condition P(c/X), and P (X) occurred consequently [2], as shown in Fig. 1.…”

“…Multidimensional properties of Big Data (BD) and nonstationary nature of Online Supervised Learning (OSL), upfront several new challenges to handle Concept Drift (CD) issue [10]. However, the contribution from the research community to mitigate the adverse effects of CD on Big Data Classification models are rarely reported [2]. Furthermore, most existing approaches are based on Shallow Learning models (ELM, SVM, and others), hence not capable of reasonably handle the non-stationary feature of BD in the OSL scenario [1].…”