tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 41

Data Mining and Knowledge Discovery Handbook, 2 Edition part 41. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 380 Alex A. Freitas ponent is typically ignored Pazzani 2000 Freitas 2006 and comprehensibility is usually evaluated by a measure of the syntactic simplicity of the classifier say the size of the rule set. The latter can be measured in an objective manner for instance by simply counting the total number of rule conditions in the rule set represented by an individual. However there is a natural way of incorporating a subjective measure of comprehensibility into the fitness function of an EA namely by using an interactive fitness function. The basic idea of an interactive fitness function is that the user directly evaluates the fitness of individuals during the execution of the EA Banzhaf 2000 . The evaluation of the user is then used as the fitness measure for the purpose of selecting the best individuals of the current population so that the EA evolves solutions that tend to maximize the subjective preference of the user. An interactive EA for attribute selection is discussed . in Terano Ishino 1998 2002 . In that work an individual represents a selected subset of attributes which is then used by a classification algorithm to generate a set of rules. Then the user is shown the rules and selects good rules and rule sets according to her his subjective preferences. Next the individuals having attributes that occur in the selected rules or rule sets are selected as parents to produce new offspring. The main advantage of interactive fitness functions is that intuitively they tend to favor the discovery of rules that are comprehensible and considered good by the user. The main disadvantage of this approach is that it makes the system considerably slower. To mitigate this problem one often has to use a small population size and a small number of generations. Another kind of criterion that has been used to evaluate the quality of classification rules in the fitness function of EAs is the surprisingness of the discovered rules. First of all it should be noted that .