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—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 evaluated for this task. However, due to inconsistent findings regarding the superiority of one(More)
The Support Vector Machine (SVM) is a powerful learning mechanism and promising results have been obtained in the field of medical diagnostics and text-categorization. However, successful applications to business oriented classification problems are still limited. Most real world data sets exhibit vast class imbalances and an accurate identification of the(More)
Corporate data mining faces the challenge of systematic knowledge discovery in large data streams to support managerial decision making. While research in operations research, direct marketing and machine learning focuses on the analysis and design of data mining algorithms, the interaction of data mining with the preceding phase of data prepro-cessing has(More)
In corporate data mining applications, cost-sensitive learning is firmly established for predictive classification algorithms. Conversely, data mining methods for regression and time series analysis generally disregard economic utility and apply simple accuracy measures. Methods from statistics and computational intelligence alike minimise a symmetric(More)
The support vector machine is a powerful classi-fier that has been successfully applied to a broad range of pattern recognition problems in various domains, e.g. corporate decision making, text and image recognition or medical diagnosis. Support vector machines belong to the group of semi-parametric classifiers. The selection of appropriate parameters,(More)
Data mining for customer relationship management involves the task of binary classification, e.g. to distinguish between customers who are likely to respond to direct mail and those who are not. The support vector machine (SVM) is a powerful learning technique for this kind of problem. To obtain good classification results the selection of an appropriate(More)
In competitive consumer markets, data mining for customer relationship management faces the challenge of systematic knowledge discovery in large data streams to achieve operational, tactical and strategic competitive advantages. Methods from computational intelligence, most prominently artificial neural networks and support vector machines, compete with(More)