CBC: clustering based text classification requiring minimal labeled data

This work presents a clustering based classification approach, where training data, including both the labeled and unlabeled data, is first clustered with the guidance of the labeled data, and demonstrates that CBC outperforms existing algorithms when the size of labeled dataset is very small.

Abstract

Semisupervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trained models to certain extent, existing methods still face difficulties when labeled data is not sufficient and biased against the underlying data distribution. We present a clustering based classification (CBC) approach. Using this approach, training data, including both the labeled and unlabeled data, is first clustered with the guidance of the labeled data. Some of unlabeled data samples are then labeled based on the clusters obtained. Discriminative classifiers can subsequently be trained with the expanded labeled dataset. The effectiveness of the proposed method is justified analytically. Our experimental results demonstrated that CBC outperforms existing algorithms when the size of labeled dataset is very small.