A Method for Attribute Selection in Inductive Learning Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental learning of concept descriptions: A method and experimental results
Machine intelligence 11
Computer-Access Security Systems Using Keystroke Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Instance-Based Learning Algorithms
Machine Learning
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Progressive partial memory learning
Progressive partial memory learning
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Selecting Examples for Partial Memory Learning
Machine Learning
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Machine Learning
A Method for Partial-Memory Incremental Learning and its Application to Computer Intrusion Detection
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Incremental learning with partial instance memory
Artificial Intelligence
Classification of intrusion detection alerts using abstaining classifiers
Intelligent Data Analysis
LEARNING DRIFTING NEGOTIATIONS
Applied Artificial Intelligence
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
Evolution and incremental learning in the iterated prisoner's dilemma
IEEE Transactions on Evolutionary Computation
Evolutionary game theoretic approach for modeling civil violence
IEEE Transactions on Evolutionary Computation
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
On-Line learning of decision trees in problems with unknown dynamics
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. We extend our previous work by combining our method for selecting extreme examples with two incremental learning algorithms, AQ11 and GEM. Using these new systems, AQ11-PM and GEM-PM, and the task computer intrusion detection, we conducted a lesion study to analyze trade-offs in performance. Results showed that, although our partial-memory model decreased predictive accuracy by 2%, it also decreased memory requirements by 75%, learning time by 75%, and in some cases, concept complexity by 10%, an outcome consistent with earlier results using our partial-memory method and batch learning.