Many fuzzy rule induction algorithms have been proposed during the past decade or so. Most of these algorithms tend to scale badly with large dimensions of the feature space because the underlying heuristics tend to constrain suboptimal features. Often noisy training instances also influence the size of the resulting rule set. In this paper an algorithm is discussed that extracts a set of so ca...

The primary goal of the research reported in this paper is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy topdown covering algorithm. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter s...

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for s...

This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule...

We examine the classification performance of fuzzy rule-based systems designed by three-objective genetic rule selection. While a single rule set is usually obtained from a single run of rule generation methods, multiple rule sets are simultaneously obtained by a single run of our rule selection method with three objectives: to maximize the number of correctly classified training patterns, to m...

One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points where to place the items inside the bins. The performance of exact and heuristic methods is actually strongly influenced by the choice of a placement rule. We introduce the Extreme Point concept and present a new Extreme Point-based rule for packing items inside ...

This research investigates an understudied decision heuristic, the majority rule. By using the rule, decision makers choose the option superior on most of the available cues. Cues are broadly defined, including advisors and attributes. We propose that decision makers are more likely to use the majority rule when encouraged to employ intra-cue comparison as opposed to intra-option integration, a...

Backpropagation based on minimization algorithms is replaced by heuristic search techniques for quantized weights. The resulting algorithm is fast, avoids local minima of the cost function, and may be used either as initialization method for standard backpropagation or as a logical rule extraction technique.

The method of brackets is a method of integration based upon a small number of heuristic rules. Some of these have been made rigorous. An example of an integral involving the Bessel function is used to motivate a new evaluation rule.

We present a preprocessing algorithm for SAT, based on the HypBinRes inference rule, and show that it does not improve the performance of a DPLL-based SAT solver with conflict recording. We also present evidence that the ineffectiveness of the preprocessing algorithm is the result of interaction with the branching heuristic used by the solver.