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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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Lightweight Privacy-Preserving Federated Incremental Decision Trees
IEEE Transactions on Services Computing, 2022Tree-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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Incremental Fuzzy Decision Tree-Based Network Forensic System
International Conference on Computational Intelligence and Security, 2005 Network 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 ...
Zaiqiang Liu, Dengguo Feng
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Research on incremental decision tree algorithm
Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011For data analysis of increase rapidly customer behavior, Web log analysis, network intrusion detection systems and other online classification system, how to quickly adapt to new samples is the key to ensure proper classification and sustainable operation.
Q. Chi
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Multi-objective Optimization for Incremental Decision Tree Learning
International Conference on Data Warehousing and Knowledge Discovery, 2012Decision tree learning can be roughly classified into two categories: static and incremental inductions. Static tree induction applies greedy search in splitting test for obtaining a global optimal model. Incremental tree induction constructs a decision model by analyzing data in short segments; during each segment a local optimal tree structure is ...
Hang Yang, Simon Fong, Yain-Whar Si
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Efflcient incremental decision tree generation for embedded applications
IEEE Conference on Cybernetics and Intelligent Systems, 2004., 2005This paper describes a frequency table-based decision tree algorithm for embedded applications. The table contains a compact statistical representation of the training set feature vectors and can be used in conjunction with a variety of learning methods.
E. Swere, D. Mulvaney, I. Sillitoe
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Decision tree usage for incremental parametric speech synthesis
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014Human speakers plan and deliver their utterances incrementally, piece-by-piece, and it is obvious that their choice regarding phonetic details (and the details' peculiarities) is rarely determined by globally optimal solutions. In contrast, parametric speech synthesizers use a full-utterance context when optimizing vocoding parameters and when ...
Timo Baumann
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Regularized and incremental decision trees for data streams
Annals of Telecommunications, 2020 Decision 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, FabrÃcio 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 ...
Chongsheng Zhang +5 more
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