Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity
Autoři:
Prem Junsawang aff001; Suphakant Phimoltares aff001; Chidchanok Lursinsap aff001
Působiště autorů:
Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
aff001
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0220624
Souhrn
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.
Klíčová slova:
Biology and life sciences – Cell biology – Cellular types – Animal cells – Neuroscience – Cellular neuroscience – Neurons – Cognitive science – Cognitive psychology – Learning – Learning and memory – Neural networks – Psychology – Biochemistry – Proteins – Protein interactions – Protein-protein interactions – Organisms – Eukaryota – Fungi – Yeast – Social sciences – Physical sciences – Mathematics – Applied mathematics – Algorithms – Research and analysis methods – Simulation and modeling – Computer and information sciences – Data management – Computer networks – Internet
Zdroje
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