In accordance with intelligent complementary strategies, a new transformer fault diagnosis method is proposed based on rough set (RS) and fuzzy set (FS) and Bayesian optimal classifier in this paper. Through RS reduction, the diagnostic decision table is greatly simplified and fault symptoms information is compressed, dramatically, and the minimal decision rules can be obtained. In the light of...

According to intelligent complementary ideas, a new transformer fault diagnosis method is proposed based on rough set (RS) and Bayesian optimal classifier in this paper. Through RS reduction, the diagnostic decision table is simplified and fault symptoms information is compressed, and the minimal decision rules can be obtained. In light of the minimal decision rules, the complexity of Bayesian ...

A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a jointprobability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, ...

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristics of natural normalization in the likelihood function, and then follow standard Gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implemen...

To maximize the benefit that can be derived from the information implicit in big data, ensemble methods generate multiple models with sufficient diversity through randomization or perturbation. A k-dependence Bayesian classifier (KDB) is a highly scalable learning algorithm with excellent time and space complexity, along with high expressivity. This paper introduces a new ensemble approach of K...

In the traditional na¨ıve Bayes classification method, training data are represented as a single table (or database relation), where each row corresponds to an example and each column to a predictor variable or a target variable. In this paper we propose a multi-relational extension of the na¨ıve Bayes classification method that is characterized by three aspects: first, an integrated approach i...

Ensemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where inco...

In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and...

We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. Then, we explore whether information about meeting context can aid classifiers’ performances. Both classifiers pe...

This paper addresses the task of choosing prior weights for models that are to be used for weighted model averaging. Models that are very similar to each other should usually be given smaller weights than models that are quite distinct. Otherwise, the importance of a model in the weighted average could be increased by augmenting the set of models with duplicates of the model or virtual duplicat...