Machine Learning
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Principles of data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A decision rule-based method for feature selection in predictive data mining
Expert Systems with Applications: An International Journal
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Learning intrusion detection: supervised or unsupervised?
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Using attack-specific feature subsets for network intrusion detection
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Positive-versus-Negative Classification for Model Aggregation in Predictive Data Mining
INFORMS Journal on Computing
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Classification modeling is commonly used for predictive data mining to create models (classifiers) that can predict the values of qualitative variables. Ensemble classification is concerned with the creation of many base classifiers which are then combined into one predictive classification model. Positive-versus-negative (pVn) classification has recently been proposed in the literature as an ensemble classification method with a potential to provide high predictive performance. Many methods of combining base model predictions for ensembles have been reported in the literature. The purpose of this paper is to report on a study that was conducted to compare four methods of combining base model predictions for pVn ensemble classification. The four methods that were studied are the max rule, min rule, sum rule and product rule. The four rules were studied for classification tree and artificial neural network pVn ensemble classification using a benchmark dataset for computer network intrusion detection systems. The main conclusion from the studies is that the sum, product and min rules provide predictive performance which is at least as high as that provided by the max rule for pVn classification.