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Tracking concept drifts in data streams has recently become a hot topic in data mining. Most of the existing work is built on a single-window-based mechanism to detect concept drifts. Due to the inherent limitation of the single-window-based mechanism, it is a challenge to handle different types of drifts. Motivated by this, a new classification algorithm based on a double-window mechanism for handling...
Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. We focus on the sentence-level sentiment classification. On the systematically analyzing the importance and difficulties of the sentence-level sentiment classification, this paper proposes a syntax tree pruning and tree kernel-based approach to sentiment classification. In our method, the...
Ensemble classification methods have shown promise for achieving higher classification accuracy for microarray data classification analysis. As noise values do exist in all microarray data even after microarray data preprocessing stage, robustness is therefore another very important criteria in addition to accuracy for evaluating reliable microarray classification algorithms. In this paper, we conduct...
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