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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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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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Pool-based active learning based on incremental decision tree
2010 International Conference on Machine Learning and Cybernetics, 2010 The pool-based active learning intends to collect the samples into the pool firstly, and selects the best informative sample from it which has no label to add into the training sets for updating the classifier secondly. This paper proposed a new method based on the incremental decision tree algorithm to measure the ambiguity of the unlabeled samples ...
Shuo Wang +3 more
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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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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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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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