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» Experiments with Cost-Sensitive Feature Evaluation
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COLING
2010
13 years 8 days ago
A Comparison of Models for Cost-Sensitive Active Learning
Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ann...
Katrin Tomanek, Udo Hahn
ICMLA
2008
13 years 6 months ago
Comparison with Parametric Optimization in Credit Card Fraud Detection
We apply five classification methods, Neural Nets(NN), Bayesian Nets(BN), Naive Bayes(NB), Artificial Immune Systems(AIS) [4] and Decision Trees(DT), to credit card fraud detectio...
Manoel Fernando Alonso Gadi, Xidi Wang, Alair Pere...
KDD
2010
ACM
287views Data Mining» more  KDD 2010»
13 years 7 months ago
Designing efficient cascaded classifiers: tradeoff between accuracy and cost
We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages. The approach relies on the idea of optimizing soft cascades. In particular, inst...
Vikas C. Raykar, Balaji Krishnapuram, Shipeng Yu
DLS
2010
146views Languages» more  DLS 2010»
13 years 3 months ago
Experiences with an icon-like expression evaluation system
The design of the Icon programming language's expression evaluation system, which can perform limited backtracking, was unique amongst imperative programming languages when c...
Laurence Tratt
TRECVID
2007
13 years 6 months ago
TRECVID 2007 High Level Feature Extraction experiments at JOANNEUM RESEARCH
This paper describes our experiments for the high level feature extraction task in TRECVid 2007. We submitted the following five runs: • A jr1 1: Baseline run using early fusio...
Roland Mörzinger, Georg Thallinger