Data structures and program design in C++
Data structures and program design in C++
Data mining: concepts and techniques
Data mining: concepts and techniques
Bayesian online classifiers for text classification and filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Classification over SQL Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
| Hi-index | 0.00 |
Classification accuracy and efficiency of an intrusion detection system (IDS) are largely affected by the discretization methods applied on continuous attributes. Cut generation is one of the methods of discretization and by applying variable number of cuts (in a partition) to the continuous attributes, different classification accuracy are obtained. In the paper to maximize accuracy of classifying network traffic data either 'normal' or 'anomaly', the proposed algorithm determines the set of cut points for each of the continuous attributes. After generation of appropriate and necessary cut points, they are mapped into corresponding intervals following centre-spread encoding technique. The learnt cut points are applied on the test data set for discretization to achieve maximum classification accuracy.