The understanding that agents have goals, and the ability to infer them, is fundamental in social cognition. However, much of our social understanding goes beyond goal attribution. Drawing on both behavioral studies throughout development, and on the limitations of past models, we propose that humans have a naïve utility calculus to reason about the costs and rewards underlying agents’ goals. W...

Intrusion detection can be considered as a classification task that attempts to classify a request to access network services as safe or malicious. Data mining techniques are being used to extract valuable information that can help in detecting intrusions. In this paper, we evaluate the performance of rule based classifiers like: JRip, RIDOR, NNge and Decision Table (DT) with Naïve Bayes (NB) a...

Classification, particularly Text Classification, is a supervised learning approach categorizing into various categories, the available training set of correctly identified observations analyzed into a set of features. There are many phases involved in classification. The main classification phase involves the use of classification algorithms or classifiers. Among the various classifiers, the N...

This paper proposes a novel naı̈ve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a dista...

BACKGROUND An antibiogram (ABG) gives the results of in vitro susceptibility tests performed on a pathogen isolated from a culture of a sample taken from blood or other tissues. The institutional cross-ABG consists of the conditional probability of susceptibility for pairs of antimicrobials. This paper explores how interpretative reading of the isolate ABG can be used to replace and improve the...

BACKGROUND In a recent paper, Mussa, Mitchell and Glen (MMG) have mathematically demonstrated that the "Laplacian Corrected Modified Naïve Bayes" (LCMNB) algorithm can be viewed as a variant of the so-called Standard Naïve Bayes (SNB) scheme, whereby the role played by absence of compound features in classifying/assigning the compound to its appropriate class is ignored. MMG have also proffered...

This research focuses on developing and implementing the Naïve Bayesian Classifier to GEAR courses at Rio Salado Community College. It demonstrates that this predictive model has good prediction accuracy of at-risk students. Predictive results across courses and cumulative gain charts show potential improvements to be made in students' academic success by focusing at high level risk students.

BACKGROUND AND PURPOSE To study the accuracy, specificity and sensitivity of the Naïve Bayesian Classifier (NBC) in the assessment of individual risk of cancer relapse or progression after radiotherapy (RT). MATERIALS AND METHODS Data of 142 brain tumour patients irradiated from 2000 to 2005 were analyzed. Ninety-six attributes related to disease, patient and treatment were chosen. Attributes...

The generalized Dirichlet distribution has been shown to be a more appropriate prior for naı̈ve Bayesian classifiers, because it can release both the negative-correlation and the equal-confidence requirements of the Dirichlet distribution. The previous research did not take the impact of individual attributes on classification accuracy into account, and therefore assumed that all attributes foll...

Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA) and a boosting naïve Bayesian tree (NBTree), is p...