PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection)

Abstract

1 Introduction and Motivation Learning classifier models is an important problem in data mining. Observations from the real world are often recorded as a set of records, each characterized by multiple attributes. Associated with each record is a categorical attribute called class. Given a training set of records with known class labels, the problem is to learn a model for the class in terms of other attributes. The goal is to use this model to predict the class of any given set of records, such that certain objective function based on the predicted and actual classes is optimized. Traditionally, the goal has been to minimize the number of misclassified records; i.e. to maximize accuracy. Various techniques exist today to build classifier models[11]. Although no single technique is proven to be the best in all situations, techniques that learn rule-based models are especially popular in the domain of data mining. This can be contributed to the easy interpretability of the rules by humans, and competitive performance exhibited by rule-based models in many application domains.