(Enter summary)
Abstract: Inductive learning and classification techniques
have been applied in many problems in diverse
areas. In this paper we describe an AI-based
approach that combines inductive learning algorithms
and meta-learning methods as a means
to compute accurate classification models for detecting
electronic fraud. Inductive learning algorithms
are used to compute detectors of anomalous
or errant behavior over inherently distributed
data sets and meta-learning methods integrate
their collective... (Update)
Context of citations to this paper: More
.... fraud detectors with extensive results (TP FP spread and cost model) from the mining of these credit card data sets can be found in [31]. 4.1 Computing Base Classifiers The first step involves the training of the base classifiers. We split each data set in 12 subsets...
...aggregate. For a binary classification problem, for example, where each classifier C i with weight 4 Andreas L. Prodromidis and Salvatore J. Stolfo w i casts a 0 vote for class y 1 and a 1 vote for class y 2 , the aggregate is given by: S(x) # K i=1 w i C i (x) # K i=1 w i...
Cited by: More
Designing Intrusion Detection Systems: Architectures.. - Mukkamala, Sung, Abraham
(Correct)
Distributed Data Mining Systems - Prodromidis (1999)
(Correct)
Intrusion Detection: A Bibliography - Mé, Michel (2001)
(Correct)
Similar documents (at the sentence level): More
14.4%: A Comparative Evaluation of Meta-Learning Strategies over.. - Prodromidis, Stolfo (1999)
(Correct)
13.0%: Effective and Efficient Pruning of Meta-Classifiers in a.. - Prodromidis, Stolfo (1999)
(Correct)
7.2%: Cost-based Modeling for Fraud and Intrusion Detection.. - Stolfo, Fan, Lee
(Correct)
Active bibliography (related documents): More All
0.0: Data Driven Formant Synthesis - Högberg (1997)
(Correct)
0.0: The Living Machine Initiative - Weng (1996)
(Correct)
0.0: Tuning Search Algorithms for Real-World Applications: A.. - Bartz-Beielstein, Markon (2004)
(Correct)
Similar documents based on text: More All
0.5: Unsupervised Profiling Methods for Fraud Detection - Bolton, Hand
(Correct)
0.4: Credit Card Fraud Detection Using Bayesian and Neural.. - Maes, Tuyls..
(Correct)
0.3: Distributed Data Mining in Credit Card Fraud Detection - Chan, Fan, Prodromidis.. (1999)
(Correct)
Related documents from co-citation: More All
7: Stacked Generalization
- Wolpert - 1992
6: Jam: Java agents for meta-learning over distributed databases
- Stolfo, Prodromidis et al. - 1997
6: Meta-learning for multistrategy and parallel learning (context) - Chan, Stolfo - 1993
BibTeX entry: (Update)
A. L. Prodromidis and S. J. Stolfo. Agent-based distributed learning applied to fraud detection. In Sixteenth National Conference on Artificial Intelligence. Submitted for publication. http://citeseer.ist.psu.edu/prodromidis99agentbased.html More
@misc{ prodromidis-agentbased,
author = "A. Prodromidis and S. Stolfo",
title = "Agent-based distributed learning applied to fraud detection",
text = "A. L. Prodromidis and S. J. Stolfo. Agent-based distributed learning applied
to fraud detection. In Sixteenth National Conference on Artificial Intelligence.
Submitted for publication.",
url = "citeseer.ist.psu.edu/prodromidis99agentbased.html" }
Citations (may not include all citations):
1262
Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.columbia.edu/~andreas/publications/publications.html): More
Extensible Resource Management For Cluster Computing - Islam, Prodromidis.. (1996)
(Correct)
A Comparative Evaluation of Meta-Learning Strategies over.. - Prodromidis, Stolfo (1999)
(Correct)
JAM: Java Agents for Meta-Learning over Distributed.. - Stolfo, Prodromidis.. (1997)
(Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC