Results 241 to 250 of about 199,658 (311)
A New Incremental Learning Technique For Decision Trees With Thresholds
SPIE Proceedings, 1989 This paper presents some basic algorithms for manipulating decision trees with thresholds. The algorithms are based on discrete decision theory. This algebraic approach to discrete decision theory, in particular, provides syntactic techniques for reducing the size of decision trees.
Jacques Robin, B. Cockett, Yunzhou Zhu
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An Increment Decision Tree Algorithm for Streamed Data
2015 IEEE Trustcom/BigDataSE/ISPA, 2015 Incremental (online) learning algorithms are methods for on-demand classification process from continuous streams of data. The main purpose is to deal with the classification task when original dataset is too large to process or when new instances of data arrive at any time.
Dariusz Jankowski, Konrad Jackowski
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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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STABLE DECISION TREES: USING LOCAL ANARCHY FOR EFFICIENT INCREMENTAL LEARNING
International Journal on Artificial Intelligence Tools, 2000 This work deals with stability in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and oscillations between similar concepts decrease learning speed. We introduce a heuristic and an algorithm with theoretical and experimental backing to tackle this problem.
Dimitris Kalles, Athanasios Papagelis
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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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A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets
, 2008 Several algorithms for induction of decision trees have been developed to solve problems with large datasets, however some of them have spatial and/or runtime problems using the whole training sample for building the tree and others do not take into account the whole training set.
Anilú Franco-Árcega +3 more
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Lightweight Privacy-Preserving Federated Incremental Decision Trees
IEEE Transactions on Services Computing, 2023Tree-based models are wildly adopted in various real-world scenarios. Recently, there is a growing interest in vertical federated tree-based model learning to build tree-based models by exploiting data from multiple organizations without violating data ...
Zhaoyang Han +3 more
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Energy Technology, 2023
Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH ...
Xingzi Qiang +3 more
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Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH ...
Xingzi Qiang +3 more
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2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006 Erick Swere +2 more
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