Cost-sensitive classification is one of the hottest research topics in data mining and machine learning. It is prevalent in many applications, such as Automatic Target Recognition (ATR), medical diagnosis, etc. However, the data in practice may be inconsistent due to dimensional reduction operation or other pre-processing, yet it is not clear how the inconsistent data affects cost-sensitive lea...

preventive maintenance scheduling of generating units is addressed as a long-term scheduling in power system studies aiming to increase the reliability incorporating cost reduction. it consists of knowing which generating units should be shut down for regular safety inspection. in this paper, a new formulation of preventive maintenance scheduling associated with cost reduction index (cri) is pr...

Data Mining and Knowledge Discovery in Databases are two new dimensions of database technology that investigate the automatic extraction for identifying hidden patterns and trends from large amount of data. Several researchers have contributed variety of algorithms for generating the classification rule by considering different cases like scalability, computation time, I/O complexity, missing a...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

finding frequent patterns plays a key role in exploring association patterns, correlation, and many other interesting relationships that are applicable in tdb. several association rule mining algorithms such as apriori, fp-growth, and eclat have been proposed in the literature. fp-growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract...

This paper describes AutoChorusCreator(ACC), a system capable of producing, in real-time, a variety of fourpart harmonies from lead sheet music. Current algorithms for generating four-part harmony have established a high standard in producing results following rules of harmony theories. However, it is still a challenging task to increase variation in the output. Detailed constraints for describ...

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to ...

Based on practical observations on rule-based inference on RDF data, we study the problem of redundancy elimination in RDF in the presence of rules (in the form of Datalog rules) and constraints (in the form of so-called tuple-generating dependencies). To this end, we investigate the influence of several problem parameters (like restrictions on the size of the rules and/or the constraints) on t...

A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose a t a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary ...