In order to defend against extraordinary intelligent attacks in the era of rapidly growing information and technology nowadays, effective and efficient intrusion detection models are needed to detect and prevent intrusion promptly. Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion attacks with reasonable accuracy and effic...

Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed t...

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...

Classification is one of the major tasks in knowledge discovery and data mining. Naive Bayes classifier, in spite of its simplicity, has proven surprisingly effective in many practical applications. In real datasets, noise is inevitable, because of the imprecision of measurement or privacy preserving mechanisms. In this paper, we develop a new approach, LinEar-Equation-based noise-aWare bAYes c...

Machine learning provides tools for automatized analysis of data. The most commonly used form of machine learning is supervised classification. A supervised classifier learns a mapping from the descriptive features of an object to the set of possible classes, from a set of features-class pairs. Once learned, it is used to predict the class for novel data instances. The Bayesian network-based su...

We compare the performance and explanation abilities of several machine learning algorithms in the problem of predicting the femoral neck fracture recovery. Among different algorithms, the semi naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving the prediction problem and show that the combined cla...

In this paper we present a generic classifier for detecting spatio-temporal feature points within video. The premise being that given a feature detector, we can learn a classifier that duplicates its functionality which is both accurate and computationally efficient. This means that feature point detection can be achieved independent of the complexity of the original interest point formulation....

PURPOSE This work aimed to improve breast screening program accuracy using automated classification. The goal was to determine if whole image features represented in the discrete cosine transform would provide a basis for classification. Priority was placed on avoiding false negative findings. METHODS Online datasets were used for this work. No informed consent was required. Programs were dev...

Bayesian network based classifiers are only able to handle discrete variables. They assume that variables are sampled from a multinomial distribution and most real-world domains involves continuous variables. A common practice to deal with continuous variables is to discretize them, with a subsequent loss of information. The continuous classifiers presented in this paper are supported by the Ga...

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this paper, Bayesian Classifier is used as a Non-linear dat...