The paper proposes a hybrid feature selection approach based on Rough sets and Bayesian network classifiers. In the approach, the classification result of a Bayesian network is used as the criterion for the optimal feature subset selection. The Bayesian network classifier used in the paper is a kind of naive Bayesian classifier. It is employed to implement classification by learning the samples...

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of ...

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...

Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Using Naive Bayesian (NB) classifiers is known as one of the best methods for supervised approaches for WSD (Mooney, 1996; Pedersen, 2000), and this model usually uses only a topic context represented by unordered words in a large context. In this paper, we show that by adding more rich...

In this paper, we propose a technique to construct a sub-optimal semi-naive Bayesian network when given a bound on the maximum number of variables that can be combined into a node. We theoretically show that our approach has a less computation cost when compared with the traditional semi-naive Bayesian network. At the same time, we can obtain a resulting sub-optimal structure according to the m...

It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian a lgorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4....

© A Heterogeneous Naive-Bayesian Classifier for Relational Databases Geetha Manjunath, M Narasimha Murty, Dinkar Sitaram HP Laboratories HPL-2009-225 Relational databases, Classification, Data Mining, RDF Most enterprise data is distributed in multiple relational databases with expert-designed schema. Application of single-table data mining techniques to distributed relational data not only inc...

The Semi-Naive Bayesian network (SNB) classifier, a probabilistic model with an assumption of conditional independence among the combined attributes, shows a good performance in classification tasks. However, the traditional SNBs can only combine two attributes into a combined attribute. This inflexibility together with its strong independency assumption may generate inaccurate distributions fo...

Settings. Our codes were written in Scala and compiled under Simple Build Tool (SBT). The programs were run on Mac OS. We test the effectiveness of our implementation in various aspects. If not mentioned explicitly, we adopt the following default settings. We report macroaveraged F1 measures, which were further averaged by ten-fold cross validations. We consider both “Bernoulli” and “Multinomia...

The naive Bayes classifier is widely used in interactive applications due to its computational efficiency, direct theoretical base, and competitive accuracy. However, its attribute independence assumption can result in sub-optimal accuracy. A number of techniques have explored simple relaxations of the attribute independence assumption in order to increase accuracy. TAN is a state-of-the-art ex...