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OcVFDT: one-class very fast decision tree for one-class classification of data streams

Published: 28 June 2009 Publication History

Abstract

Current research on data stream classification mainly focuses on supervised learning, in which a fully labeled data stream is needed for training. However, fully labeled data streams are expensive to obtain, which make the supervised learning approach difficult to be applied to real-life applications. In this paper, we model applications, such as credit fraud detection and intrusion detection, as a one-class data stream classification problem. The cost of fully labeling the data stream is reduced as users only need to provide some positive samples together with the unlabeled samples to the learner. Based on VFDT and POSC4.5, we propose our OcVFDT (One-class Very Fast Decision Tree) algorithm. Experimental study on both synthetic and real-life datasets shows that the OcVFDT has excellent classification performance. Even 80% of the samples in data stream are unlabeled, the classification performance of OcVFDT is still very close to that of VFDT, which is trained on fully labeled stream.

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cover image ACM Conferences
SensorKDD '09: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
June 2009
150 pages
ISBN:9781605586687
DOI:10.1145/1601966
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 June 2009

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Author Tags

  1. Hoeffding bound
  2. decision trees
  3. incremental learning
  4. one-class data streams

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  • (2021)Random Fuzzy Granular Decision TreeMathematical Problems in Engineering10.1155/2021/55786822021(1-17)Online publication date: 9-Jun-2021
  • (2021)Positive-unlabeled learning in bioinformatics and computational biology: a brief reviewBriefings in Bioinformatics10.1093/bib/bbab461Online publication date: 3-Nov-2021
  • (2021)A Survey on Streaming Data Analytics: Research Issues, Algorithms, Evaluation Metrics, and PlatformsProceedings of International Conference on Big Data, Machine Learning and Applications10.1007/978-981-33-4788-5_9(101-118)Online publication date: 23-Mar-2021
  • (2020)A Dynamic and Scalable Decision Tree Based Mining of Educational DataCognitive Analytics10.4018/978-1-7998-2460-2.ch044(841-866)Online publication date: 2020
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  • (2020)A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and ChallengesJournal of Grid Computing10.1007/s10723-020-09526-yOnline publication date: 4-Oct-2020
  • (2020)Empirical Analysis of Classification Algorithms in Data Stream MiningInternational Conference on Innovative Computing and Communications10.1007/978-981-15-5113-0_53(657-669)Online publication date: 2-Aug-2020
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