Prior studies of technology non-use demonstrate the need for approaches that go beyond a
simple binary distinction between users and non-users. This paper proposes a set of two …

Naïve Bayes learners are widely used, efficient, and effective supervised learning methods
for labeled datasets in noisy environments. It has been shown that naïve Bayes learners …

Most previous studies on rough sets focused on attribute reduction and decision rule mining
on a single concept level. Data with attribute value taxonomies (AVTs) are, however …

Classification and rule induction are key topics in the fields of decision making and
knowledge discovery. The objective of this study is to present a new algorithm developed for …

Taxonomies are essential tools for fast information retrieval and classification of knowledge.
Many existing techniques for automatic taxonomy generation strongly depend on the …

Traditionally, the algorithm of ID3 takes the information gain as a standard of expanding
attributes. During the process of selection of expanded attributes, attributes with more values …

Grid computing is becoming a mainstream technology for large-scale resource sharing and
distributed system integration. One underlying challenge in Grid computing is the resource …

The aim of the article is to analyse and develop an ontology-based classification system
methodology that uses decision tree learning with statement propositionalized attributes …

Background The amount of available and potentially significant data describing study
subjects is ever growing with the introduction and integration of different registries and data …

In this paper, we consider the problem of generating concise but accurate naive Bayes
classifiers using taxonomy of propositionalized attributes. For the problem, we introduce …