D. Aha

Publications292
h-index49
Citations14,461
Highly Influential Citations855
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Instance-Based Learning Algorithms
TLDR
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Instance-based learning algorithms
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed…
Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms
  • D. Aha
  • Computer Science
    Int. J. Man Mach. Stud.
  • 1 February 1992
TLDR
This paper presents a comprehensive sequence of three incremental, edited nearest neighbor algorithms that tolerate attribute noise, determine relative attribute relevances, and accept instances described by novel attributes.
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
TLDR
A class of weight-setting methods for lazy learning algorithms which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings.
A Comparative Evaluation of Sequential Feature Selection Algorithms
TLDR
Positive empirical results are reported on variants of sequential feature selection that might be more appropriate for some performance tasks, and it is argued for their serious consideration in similar learning tasks.
Generalizing from Case studies: A Case Study
  • D. Aha
  • Mathematics, Computer Science
    ML
  • 1 July 1992
TLDR
An empirical method for generalizing results from case studies and an example application is described, which yields rules describing when some algorithms significantly outperform others on some dependent measures.
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
TLDR
A plan retrieval algorithm is introduced that, by using three key sources of domain knowledge, removes the assumption of a static opponent and significantly outperforms the best among a set of genetically evolved plans when tested against random Wargus opponents.
INSTANCE-BASED LEARNING : Nearest Neighbour with Generalisation
Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest…
Refining Conversational Case Libraries
TLDR
This work describes an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance.
A study of instance-based algorithms for supervised learning tasks: mathematica:l
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