The Strength of Weak Learnability
Machine Learning
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Democracy in neural nets: voting schemes for classification
Neural Networks
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Boosting and other ensemble methods
Neural Computation
A framework for reasoning about the human in the loop
UPSEC'08 Proceedings of the 1st Conference on Usability, Psychology, and Security
Anomaly Detection Support Vector Machine and Its Application to Fault Diagnosis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Decision support for improved service effectiveness using domain aware text mining
Knowledge-Based Systems
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Data mining has been a key technology in the warranty sector for mass manufacturers to understand and improve product quality, reliability and durability. Cost savings is an important aspect of business which calls for processes that are error proof. Pattern classification methods applied to the diagnostic data could help build error proof processes by improving the diagnostic technology. In this paper we present a case study from the automotive warranty and service domain involving a human-in-the-loop decision support system (HIL-DSS). The automotive manufacturers offer warranties on products, made of parts from different suppliers, and rely on a dealer network to assess warranty claims. The dealers use diagnostic equipment manufactured by third parties and also draw on their own expertise. In addition, a subject matter expert (SME) assesses these collective decisions to distinguish between inaccurate diagnoses by the dealers or an inadequate decision algorithm in the diagnostic equipment. Altogether this makes a comprehensive HIL-DSS. The proposed methodology continuously learns from collective decision making systems, enhances the diagnostic equipment, adds to the knowledge of dealers and minimizes the SME involvement in the review process of the overall system. Improving the diagnostic equipment helps in better warranty servicing, whereas improvements in the human expert knowledge help prevent field error and avoid customer dissatisfaction due to improper fault diagnosis.