Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

Abstract

Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.

Employing chunk size adaptation to overcome concept drift

“…This method is known as batch-based or chunk-based learning. Choosing the proper size of the chunk is crucial because it may significantly affect the classification [Junsawang et al, 2019]. Unfortunately, the unpredictable appearance of the concept drift makes it difficult.…”

Employing chunk size adaptation to overcome concept drift

“…This method is known as batch-based or chunk-based learning. Choosing the proper size of the chunk is crucial because it may significantly affect the classification [54]. Unfortunately, the unpredictable appearance of the concept drift makes it difficult.…”

Inter-release defect prediction with feature selection using temporal chunk-based learning: An empirical study

“…After some duration at time , a data chunk enters in the chunk-based learning process where the feature space and decision boundary have changed for the release . The term "chunk-based learning" refers to learning the data as chunk based on the "discard-after-learn" concept [48]. Here, the data chunk is used for learning only once and then discarded from the learning process to keep the memory space available for the next data chunk.…”