Authors: Zou Yu; Qin Zhong Ping
Addresses: School of Computer Science and Network Security, Dongguan University of Technology, 523808, Dongguan, China ' School of Software, Huazhong University of Science and Technology, 430000, Wuhan, China
Abstract: To realise multi-label classification of text and meanwhile reduce calculation complexity and keep classification precision, dimensionality-reduction clustering method for fuzzy association of text multi-label based on cluster classification has been proposed. In text classification, it usually involves enormous feature numbers, which may cause curse of dimensionality. In addition, classification region can not always keep convex characteristics. It can be non-convex region composed of several overlapping or intersecting sub-regions. Above mentioned automatic classification system may require enormous memory requirement or has poor classification performance. Hence, new multi-label text classification method is proposed to overcome these problems in combination with fuzzy association technology. Fuzzy association evaluation is adopted to transform high-dimension text to low-dimension fuzzy association vector, thus avoiding curse of dimensionality. Experiment results show that the proposed method can more effectively classify text multi-label problem.
Keywords: fuzzy transformation; key data; integration clustering; cloud data; data analysis.
International Journal of Reasoning-based Intelligent Systems, 2018 Vol.10 No.2, pp.90 - 95
Available online: 09 Jun 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article