Abstract
Proposes a rule-based inductive learning algorithm called multiscale classification (MSC). It can be applied to any N-dimensional real or binary classification problem to classify the training data by successively splitting the feature space in half. The algorithm has several significant differences from existing rule-based approaches: learning is incremental, the tree is non-binary, and backtracking of decisions is possible to some extent. The paper first provides background on current machine learning techniques and outlines some of their strengths and weaknesses. It then describes the MSC algorithm and compares it to other inductive learning algorithms with particular reference to ID3, C4.5, and back-propagation neural networks. Its performance on a number of standard benchmark problems is then discussed and related to standard learning issues such as generalization, representational power, and over-specialization.
Keywords
Computer scienceArtificial intelligenceMachine learningInductive biasBinary classificationBacktrackingClassifier (UML)GeneralizationArtificial neural networkAlgorithmSupport vector machineMathematicsMulti-task learning
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 18
- Issue
- 2
- Pages
- 124-137
- Citations
- 50
- Access
- Closed
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Cite This
Brian C. Lovell,
Andrew P. Bradley
(1996).
The multiscale classifier.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 18
(2)
, 124-137.
https://doi.org/10.1109/34.481538
Identifiers
- DOI
- 10.1109/34.481538