Results 201 to 210 of about 112,281 (246)
An Improved Algorithm for Incremental Induction of Decision Trees
, 1994 This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called 'slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree.
Paul E. Utgoff
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Multi-resident Activity Recognition Using Incremental Decision Trees
, 2014 The 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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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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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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Regularized and incremental decision trees for data streams
Annals of Telecommunications, 2020Decision trees are a widely used family of methods for learning predictive models from both batch and streaming data. Despite depicting positive results in a multitude of applications, incremental decision trees continuously grow in terms of nodes as new data becomes available, i.e., they eventually split on all features available, and also multiple ...
Jean Paul Barddal, Fabrício Enembreck
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On Incremental Learning for Gradient Boosting Decision Trees
Neural Processing Letters, 2019Boosting algorithms, as a class of ensemble learning methods, have become very popular in data classification, owing to their strong theoretical guarantees and outstanding prediction performance. However, most of these boosting algorithms were designed for static data, thus they can not be directly applied to on-line learning and incremental learning ...
Yuan Zhang +5 more
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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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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 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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A Review: The Effects of Imperfect Data on Incremental Decision Tree
2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2014Decision tree, as one of the most widely used methods in data mining, has been used in many realistic application. Incremental decision tree handles streaming data scenario that is applicable for big data analysis. However, imperfect data are unavoidable in real-world applications.
Cai Yuan +3 more
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