IEEE Transactions on Pattern Analysis and Machine Intelligence
The application of AdaBoost for distributed, scalable and on-line learning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Classification for Imprecise Environments
Machine Learning
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Prediction Markets as Decision Support Systems
Information Systems Frontiers
Combinatorial Information Market Design
Information Systems Frontiers
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
IEEE Intelligent Systems
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
A dynamic pari-mutuel market for hedging, wagering, and information aggregation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Constructing Ensembles from Data Envelopment Analysis
INFORMS Journal on Computing
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
Information market based recommender systems fusion
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
An introduction to artificial prediction markets for classification
The Journal of Machine Learning Research
| Hi-index | 0.01 |
Improved classification performance has practical real-world benefits ranging from improved effectiveness in detecting diseases to increased efficiency in identifying firms that are committing financial fraud. Multiclassifier combination (MCC) aims to improve classification performance by combining the decisions of multiple individual classifiers. In this paper, we present information market-based fusion (IMF), a novel multiclassifier combiner method for decision fusion that is based on information markets. In IMF, the individual classifiers are implemented as participants in an information market where they place bets on different object classes. The reciprocals of the market odds that minimize the difference between the total betting amount and the potential payouts for different classes represent the MCC probability estimates of each class being the true object class. By using a market-based approach, IMF can adjust to changes in base-classifier performance without requiring offline training data or a static ensemble composition. Experimental results show that when the true classes of objects are only revealed for objects classified as positive, for low positive ratios, IMF outperforms three benchmarks combiner methods, majority, average, and weighted average; for high positive ratios, IMF outperforms majority and performs on par with average and weighted average. When the true classes of all objects are revealed, IMF outperforms weighted average and majority and marginally outperforms average.