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Incremental Fuzzy Decision Trees
, 2002 We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification and generalization of data; it utilizes fuzzy logic, that provides for a fine grained description of classified items adequate for human reasoning; and it is incremental, allowing rapid ...
Marina Guetova +2 more
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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.
Fangming Ye +3 more
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Multi-resident Activity Recognition Using Incremental Decision Trees
International Conference on Adaptive and Intelligent Systems, 2014The present paper proposes the application of decision trees to model activities of daily living in a multi-resident context. An extension of ID5R, called E-ID5R, is proposed. It augments the leaf nodes and allows such nodes to be multi-labeled. E-ID5R induces a decision tree incrementally to accommodate new instances and new activities as they become ...
Markus Prossegger, Abdelhamid Bouchachia
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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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Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 2012How to extract meaningful information from big data has been a popular open problem. Decision tree, which has a high degree of knowledge interpretation, has been favored in many real world applications. However noisy values commonly exist in high-speed data streams, e.g. real-time online data feeds that are prone to interference.
Hang Yang, Simon Fong
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Incremental tuning of fuzzy decision trees
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 2012Handling stream data or temporal data is a difficult task and brings out a lot of problems to classical learning algorithms as the decision tree construction algorithms. In that context, incremental algorithms have been proposed but they often lie on the frequent reconstruction of the decision tree when this one provides a high number of misclassified ...
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Incremental and Interpretable Learning Analytics Through Fuzzy Hoeffding Decision Trees
2023Abstract Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and theeducational scenario is not an exception. However, the usage of the so-called explainable ArtificialIntelligence, even if desirable, is still limited, especially whenever we consider educational datasets.Moreover, the time dimension is ...
Gabriella Casalino +3 more
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Pacific Asia Workshop on Intelligence and Security Informatics, 2009 Weiyi Yu, Hahn-Ming Lee
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An Increment Decision Tree Algorithm for Streamed Data
2015 IEEE Trustcom/BigDataSE/ISPA, 2015Incremental (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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