Discretization is an essential preprocessing technique used in many knowledge discovery
and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete …

Heart disease is the leading cause of death in the world over the past 10 years. Researchers
have been using several data mining techniques to help health care professionals in the …

Quantitative attributes are usually discretized in Naive-Bayes learning. We establish simple
conditions under which discretization is equivalent to use of the true probability density …

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion,
LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by …

Discretization is a popular approach to handling numeric attributes in machine learning. We
argue that the requirements for effective discretization differ between naive-Bayes learning …

Semantic-based image retrieval has attracted great interest in recent years. This paper
proposes a region-based image retrieval system with high-level semantic learning. The key …

In this paper, we address the flexible job-shop scheduling problem (FJSP) with release
times for minimising the total weighted tardiness by learning dispatching rules from …

HEp-2 cells are used for the identification of antinuclear autoantibodies (ANAs). They allow
for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by …

Prior Publication Data The present disclosure includes systems and techniques relat US
2007/0208693 A1 Sep. 6, 2007 ing to representation and retrieval of data structures in data …

RECEIVE ASEED ONTOLOGY indication of symbolic knowledge for the given category; and
populating the first ontology with new features to form a second ontology, the populating …