Results 221 to 230 of about 112,281 (246)
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2008 Fourth International Conference on Networked Computing and Advanced Information Management, 2008
This paper proposed a diagnostic supporting tool - i+DiaKAW (intelligent and interactive knowledge acquisition workbench), which automatically extracts useful knowledge from massive medical data by applying various data mining techniques for supporting real medical diagnosis.
Yiping Li, Fai Wong, Sam Chao
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This paper proposed a diagnostic supporting tool - i+DiaKAW (intelligent and interactive knowledge acquisition workbench), which automatically extracts useful knowledge from massive medical data by applying various data mining techniques for supporting real medical diagnosis.
Yiping Li, Fai Wong, Sam Chao
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Incrementally Optimized Decision Tree for Mining Imperfect Data Streams
2012The Very Fast Decision Tree (VFDT) is one of the most important classification algorithms for real-time data stream mining. However, imperfections in data streams, such as noise and imbalanced class distribution, do exist in real world applications and they jeopardize the performance of VFDT.
Hang Yang, Simon Fong
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Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016Board-level functional fault diagnosis is needed for high-volume production to improve product yield. However, to ensure diagnosis accuracy and effective board repair, a large number of syndromes must be used. Therefore, the diagnosis cost can be prohibitively high due to the increase in diagnosis time and the complexity of test execution and analysis.
Zhaobo Zhang +3 more
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Incremental Fuzzy Decision Tree-Based Network Forensic System
2005Network forensic plays an important role in the modern network environment for computer security, but it has become a time-consuming and daunting task due to the sheer amount of data involved. This paper proposes a new method for constructing incremental fuzzy decision trees based on network service type to reduce the human intervention and time-cost ...
Dengguo Feng, Zaiqiang Liu
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Improving the Accuracy of Incremental Decision Tree Learning Algorithm via Loss Function
2013 IEEE 16th International Conference on Computational Science and Engineering, 2013Hoeffding's bound (HB) has been widely used for node splitting in incremental decision tree algorithms. Many decision-tree algorithms adopt a sliding-window technique to detect concept drift when mining changing data streams. This paper presents a novel node-splitting approach that replaces the traditional HB with a new measure.
Simon Fong, Hang Yang
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Incremental Response Modeling Based on Segmentation Approach Using Uplift Decision Trees
2016Data mining methods have been successfully used in direct marketing to model the behavior of responders. But these response models do not take in account, the behavior of customers who would take an action irrespective of marketing action. Redundant marketing communications sometimes annoy the customer and reduce the brand value of the company ...
Shruti Agrawal +2 more
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Incremental possibilistic decision trees in non-specificity approach
Data Science and Knowledge Engineering for Sensing Decision Support, 2018Zied Elouedi, Mohamed Sofien Boutaib
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, 2006