C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Classification as an Ensemble Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
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
Adaptive mixtures of local experts
Neural Computation
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
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
Empirical comparison of four classifier fusion strategies for positive-versus-negative ensembles
Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment
INFORMS Journal on Computing
Positive-versus-Negative Classification for Model Aggregation in Predictive Data Mining
INFORMS Journal on Computing
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Classification modeling is one of the methods commonly employed for predictive data mining. Ensemble classification is concerned with the creation of many base models which are combined into one model for purposes of increasing classification performance. This paper reports on a study which was conducted to establish whether the use of information in the confusion matrix of a single classification model could be used as a basis for the design of ensemble base models that provide high predictive performance. Positiveversus-negative (pVn) classification was studied as a method of base model design. Confusion graphs were used as input to an algorithm that determines the classes for each base model. Experiments were conducted to compare the levels of diversity provided by all-classes-at-once (ACA) and pVn base models using a statistical measure of dis-similarity. Experiments were also conducted to compare the performance of pVn ensembles, ACA ensembles, and single kclass models using classification trees and multi-layer perceptron artificial neural networks. The experimental results demonstrated that even though ACA base models provide a higher level of diversity than pVn base models, the diversity does result in higher predictive performance. The experimental results also demonstrated that pVn ensemble models can provide predictive performance that is higher than that of single k-class models and ACA ensemble models.