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Displaying 1-9 out of 9 total
Business Rule Patterns and Their Application to Process Analytics
Found in: 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW)
By Filip Caron,Jan Vanthienen,Bart Baesens
Issue Date:September 2013
pp. 13-20
The advanced rule-based compliance checking approach enables a timely investigation of a complete set of enriched process event data. By providing more than sixty formally grounded business rule patterns, we are able to remove the partial fit between the a...
A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models
Found in: IEEE Transactions on Knowledge and Data Engineering
By Thomas Verbraken,Wouter Verbeke,Bart Baesens
Issue Date:May 2013
pp. 961-973
The interest for data mining techniques has increased tremendously during the past decades, and numerous classification techniques have been applied in a wide range of business applications. Hence, the need for adequate performance measures has become more...
Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers
Found in: IEEE Transactions on Software Engineering
By Karel Dejaeger,Thomas Verbraken,Bart Baesens
Issue Date:February 2013
pp. 237-257
Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the ...
Data Mining Techniques for Software Effort Estimation: A Comparative Study
Found in: IEEE Transactions on Software Engineering
By Karel Dejaeger,Wouter Verbeke,David Martens,Bart Baesens
Issue Date:March 2012
pp. 375-397
A predictive model is required to be accurate and comprehensible in order to inspire confidence in a business setting. Both aspects have been assessed in a software effort estimation setting by previous studies. However, no univocal conclusion as to which ...
Software Effort Prediction Using Regression Rule Extraction from Neural Networks
Found in: Tools with Artificial Intelligence, IEEE International Conference on
By Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
Issue Date:October 2010
pp. 45-52
Neural networks are often selected as tool for software effort prediction because of their capability to approximate any continuous function with arbitrary accuracy. A major drawback of neural networks is the complex mapping between inputs and output, whic...
Fraud Detection in Statistics Education Based on the Compendium Platform and Reproducible Computing
Found in: Computer Science and Information Engineering, World Congress on
By Patrick Wessa, Bart Baesens
Issue Date:April 2009
pp. 50-54
This paper focuses on a newly developed method to detect fraud in empirical papers that are submitted by students. The proposed solution is based on the Compendium Platform and Reproducible Computing which allows the educator to build e-learning environmen...
Decompositional Rule Extraction from Support Vector Machines by Active Learning
Found in: IEEE Transactions on Knowledge and Data Engineering
By David Martens, Bart Baesens, Tony Van Gestel
Issue Date:February 2009
pp. 178-191
Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the...
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
Found in: IEEE Transactions on Software Engineering
By Stefan Lessmann, Bart Baesens, Christophe Mues, Swantje Pietsch
Issue Date:July 2008
pp. 485-496
Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been ev...
Determining Process Model Precision and Generalization with Weighted Artificial Negative Events
Found in: IEEE Transactions on Knowledge and Data Engineering
By Seppe K. L. M. vanden Broucke,Jochen De Weerdt,Jan Vanthienen,Bart Baesens
Issue Date:August 2014
pp. 1-1
Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as capt...