Simple-Random-Sampling-Based Multiclass Text Classification Algorithm

Abstract EN

Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications.

The space-time overhead of the algorithms must be concerned about the era of big data.

Through the investigation of the token frequency distribution in a Chinese web document collection, this paper reexamines the power law and proposes a simple-random-sampling-based MTC (SRSMTC) algorithm.

Supported by a token level memory to store labeled documents, the SRSMTC algorithm uses a text retrieval approach to solve text classification problems.

The experimental results on the TanCorp data set show that SRSMTC algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements.