Acceleration of Naive-Bayes algorithm on multicore processor for massive text classification

Zhou, Lijun; Yu, Zhou; Lin, Jie; Zhu, Shasha; Shi, Weijing; Zhou, Haijie; Song, Kunpeng; Zeng, Xiaoyang

doi:10.1109/isicir.2014.7029490

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…Naïve Bayes can be accelerated using GPU for automatic document classification. It uses different threads running during the two steps of model generation: calculation of the probability of the terms within classes, and calculation of document term frequencies [28,29]. SVM performs classification by finding the separating hyperplane that has the largest margin between two classes.…”

Section: Related Workmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

SN Appl. Sci.

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In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

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Section: Related Workmentioning

confidence: 99%