A non-canonical hybrid metaheuristic approach to adaptive data stream classification

Abstract

Data stream classification techniques have been playing an important role in big data analytics recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices and sensors, websites and social network feeds). Streaming data is often prone to evolution over time. In this context, the main challenge for computational models is to adapt to changes, known as concept drifts, using data mining and optimisation techniques. We present a novel ensemble technique called RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size, each created by randomly selecting a certain percentage of features from a pool of features of the target data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt to different concept drifts; it allows good performing types to grow and poor performing ones to shrink in size. In addition, the selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. PSO allows the types in each layer to go towards local (within the same type) $ Fully documented templates are available in the elsarticle package on CTAN.

Incremental Learning Framework for Mining Big Data Stream

“…Most of the existing data stream classification methods used ensemble learning methods due to their flexibility in updating the classification scheme, like retraining, removing, and adding the constituent classifiers [32][33][34][35]. Most of these methods are trustworthy than the single classifier schemes particularly in the non-stationary environments [5,36]. The dynamic weighted majority (DWM) effectively maintains the ensemble of the classifier with the weighted majority vote model.…”

“…They failed to integrate small-sized clusters, as they may hamper the cluster quality in terms of interpretability and structure. Ghomeshi et al [5] introduced an ensemble method for various concept drifts in the process of data stream classification based on particle swarm optimization (PSO) and replicator dynamics algorithm (RD). This method was based on three-layer architecture that generated classification types with varying size.…”

“…As such, several research works are concentrated more on the issues of data stream classification, especially in the non-stationary data. The main issue in the classification process is the utilization of various concept drifts during the changes of data distribution based on time in unforeseen ways [3][4][5][6].…”

Markov Boundary Learning With Streaming Data for Supervised Classification

“…For ensemble learning methods, the most common approaches are Bagging and Bosting, which are firstly proposed by Oza and Russell [20], like OBA [19] and LB [4]. In many environment of data mining, ensemble learning methods can achieve the best performance, such as ARF [12], [13], RED-POS [11] and KUE [6]. And ERudesD 2 s [5] is a better rule-based classification method.…”

RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification

“…RED-PSO [14] is an implicit approach that couples the Replicator Dynamics with a modification of Particle Swarm Optimisation algorithm to seamlessly adapt to different concept drifts. In this approach, different classification types act as particles in PSO algorithms and the aim is to move the particles towards the global and local optimal solutions in each iteration of the algorithm.…”