Heuristics and Optimization for Knowledge Discovery
Ruhul A. Sarker, Hussein A. Abbass, Charles Sinclair Newton
Idea Group Pub., 2002 - Business & Economics - 290 pages
With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.
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A Heuristic Algorithm for Feature Selection Based
CostSensitive Classification using Decision Trees
Heuristic SearchBased Stacking of Classifiers
10 other sections not shown
accuracy AdaBoost AdaCost application approach Artificial Intelligence artificial neural networks Bayesian boosting chapter client cluster analysis complex components Computer Science cost-sensitive cross entropy CSB1 CSB2 data mining data set databases decision tree decision tree induction defined described developed DGLC distribution engine Equation error function estimate evaluation example Fayyad feature selection Figure Gamma test genetic algorithms GLC+ gradient descent heuristic hidden nodes high cost errors informative features interface Knowledge Discovery knowledge extracted large data sets learning algorithms linear machine learning matrix medical data mining MetaCost minimal minimum expected cost mixed incomplete data naive Bayes classifier neural network model noise number of hidden objects obtained optimization parameters pattern recognition performance prediction probability problem procedure regression rough clustering rough sets rules sample shopping orientation similarity matrix solution statistical subset Table techniques training set TREPAN University values variants weights