ASSOCIATIVE-CLASSIFICATION-METHOD-BASED-ON-RULE-PRUNING-FOR-CLASSIFICATION-OF-DATASETS
A rule-based classification approach called Associative Classification (AC) normally constructs accurate classifiers from supervised learning data sets in data mining. It extracts "If-Then" rules, and associates with two computed parameters each of the generated rules; support and confidence. These two parameters are used to determine the dominance of the laws during the creation of a classification. In current AC algorithms, all of its corresponding training data is discarded whenever a rule is inserted into a classifier. The discarded data, however, are actually used to calculate support and confidence for other rules and will affect other lower-ranked rules, as rules normally have common examples of training data. Use static support and confidence will result in very large, less accurate classifiers. Therefore, it is necessary to provide a method that modifies the support and confidence of other rules. This paper proposes a new procedure called Active Pruning Rules (APR) to overcome the above problem, in order to further improve the performance of the classifiers-especially predictive accuracy and reduction of rule redundancy. The experimental results obtained from a variety of data sets from the University of California Irvine (UCI) and real data set for adult autism classification showed that APR is highly competitive with other AC and rule-based classifiers and also produces smaller and more accurate classifiers.
Results:
Average number of rules between CBA and APR
Time took to build classifier for CBA and APR (in ms)
CBA accuracy vs APR accuracy
ASD dataset accuracy between CBA and APR algorithm.
Quick start
cd APR
set dataset path in validation.py
python validation.py