Abstract. Naive Bayes is a probability-based classification method which is based on the assumption that attributes are conditionally mutually independent given the class label. Much research has been focused on improving the accuracy of Naïve Bayes via eager learning. In this paper, we propose a novel lazy learning algorithm, Selective Neighbourhood based Naïve Bayes (SNNB). SNNB computes different distance neighborhoods of the input new object, lazily learns multiple Naïve Bayes classifiers, and uses the classifier with the highest estimated accuracy to make decision. The results of our experiments on 26 datasets show that our proposed SNNB algorithm outperforms Naïve Bayes, and state-of-the-art classification methods NBTree, CBA, and C4.5 in terms of accuracy as well as efficiency.

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Concept lattice model, the core structure in Formal Concept Analysis, has been successfully applied in software engineering and knowledge discovery. In this paper, we integrate the simple base classifier (Naïve Bayes or Nearest Neighbor) into each node of the concept lattice to form a new composite classifier. We develop two new classification systems, CLNB and CLNN, that employ efficient constraints to search for interesting patterns and voting strategy to classify a new object. CLNB integrates the Naïve Bayes base classifier into concept nodes while CLNN incorporates the Nearest Neighbor base classifier into concept nodes.Experimental results indicate that these two composite classifiers greatly improve the accuracy of their corresponding base classifier. In addition, CLNB even outperforms three other state-of-art classification methods, NBTree, CBA and C4.5 Rules.

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Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.