Papers by Keyword: Naïve Bayesian Classifier

Abstract: Increasingly serious environmental pollution,trying to find a effective method to control NOx emission become more importance. Under this background, this paper adopts the naïve Bayesian classifier method which built on the basis of the probability density function to forecasting the NOx emission of diesel engine. This paper proposes a new approach to weight the super-parent one dependence estimators, and uses the UCI datasets to verify the validity of the proposed method. Finally, apply this diagnosis technology to the collected WD615 diesel engine data. The comparison experiments with other algorithms demonstrate the effectiveness of the proposed method.

Abstract: In view of complex background of cotton blind stinkbug hazard region and the difficulty in segmentation and classification under natural conditions, an automatic classification method of cotton blind stinkbug hazard level was proposed. In this method, crop regions and disease regions of cotton were extract respectively by H+a*+b* component and Otsu segmentation method based on blind stinkbug hazard cotton leaves. Adhesion cotton leaves separated by Watershed segmentation method and cotton leaf area hazard by blind stinkbug extracted. According to cotton blind the stinkbug hazard rating standard, combination Naive Bayes classifier and color, texture and shape features extracted from images to classify the hazard rating of the blind stinkbug. The results showed that the model classification correct rate was 90.0%, it could classify the hazard rating of the cotton blind stinkbug and provide technical support for the prevention and treatment of the cotton blind stinkbug.

Abstract: The Naive Bayesian classifier is the least sensitive to missing data in the methods of data mining. However, missing data still affects the accuracy of the classifiers before the dataset which contains missing data used to train in the classifier. We can fill up the missing data to gain a full dataset. This paper introduces a random patch algorithm based on Markov Transition probability. This algorithm can fill up the dataset which contains missing data to a full one. Using the full dataset to classification can increase the accuracy of the classifier. We perform experiments to prove the algorithm effectiveness.

Abstract: Redundant images currently abundant in World Wide Web pages need to be removed in order to transform or simplify the Web pages for suitable display in small-screened devices. Classifying removable images on the Web pages according to their uniqueness of content will allow simpler representation of Web pages. For such classification, machine learning based methods can be used to categorize images into two groups; eliminable and non-eliminable. We use two representative learning methods, the Naïve Bayesian classifier and C4.5 decision trees. For our Web image classification, we propose new features that have expressive power for Web images to be classified. We apply image samples to the two classifiers and analyze the results. In addition, we propose an algorithm to construct an optimized subset from a whole feature set, which includes most influential features for the purposes of classification. By using the optimized feature set, the accuracy of classification is found to improve markedly.