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In this paper, we investigate a comprehensive learning algorithm for text classification without pre-labeled training set based on incremental learning. In order to overcome the high cost in getting labeled training examples, this approach reforms fuzzy partition clustering to obtain a small quantity of labeled training data. Then the incremental learning of Bayesian classifier is applied. The model of the proposed classifier is composed of a Naïve-Bayes-based incremental learning algorithm and a modified fuzzy partition clustering method. For improved efficiency, a feature reduction is designed based on the Quadratic Entropy in Mutual Information. We perform experiments to demonstrate the performance of the approach, and the results show that our approach is feasible and effective. [ABSTRACT FROM AUTHOR]

Detecting concept drifts and reducing the impact from the noise in real applications of data streams are challenging but valuable for inductive learning. It is especially a challenge in a light demand on the overheads of time and space. However, though a great number of inductive learning algorithms based on ensemble classification models have been proposed for handling concept drifting data streams, little attention has been focused on the detection of the diversity of concept drifts and the influence from noise in data streams simultaneously. Motivated by this, we present a new light-weighted inductive algorithm for concept drifting detection in virtue of an ensemble model of random decision trees (named CDRDT) to distinguish various types of concept drifts from noisy data streams in this article. We use variably small data chunks to generate random decision trees incrementally. Meanwhile, we introduce the inequality of Hoeffding bounds and the principle of statistical quality control to detect the different types of concept drifts and noise. Extensive studies on synthetic and real streaming data demonstrate that CDRDT could effectively and efficiently detect concept drifts from the noisy streaming data. Therefore, our algorithm provides a feasible reference framework of classification for concept drifting data streams with noise. [ABSTRACT FROM AUTHOR]