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Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
  • R. Holte
  • Computer Science
  • Machine Learning
  • 1 April 1993
This article reports an empirical investigation of the accuracy of rules that classify examples on the basis of a single attribute. On most datasets studied, the best of these very simple rules is asExpand
  • 1,587
  • 107
  • Open Access
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community.Expand
  • 1,070
  • 49
  • Open Access
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
This paper takes a new look at two sampling schemes commonly used to adapt machine learning algorithms to imbalanced classes and misclassification costs. It uses a performance analysis techniqueExpand
  • 649
  • 31
Approximating Game-Theoretic Optimal Strategies for Full-scale Poker
The computation of the first complete approximations of game-theoretic optimal strategies for full-scale poker is addressed. Several abstraction techniques are combined to represent the game ofExpand
  • 244
  • 28
  • Open Access
Concept Learning and the Problem of Small Disjuncts
Ideally, definitions induced from examples should consist of all, and only, disjuncts that are meaningful (e.g., as measured by a statistical significance test) and have a low error rate. ExistingExpand
  • 406
  • 27
  • Open Access
Cost curves: An improved method for visualizing classifier performance
This paper introduces cost curves, a graphical technique for visualizing the performance (error rate or expected cost) of 2-class classifiers over the full range of possible class distributions andExpand
  • 274
  • 23
  • Open Access
The pinwheel: a real-time scheduling problem
Some satellites transmit a piece of information for a set duration, then proceed with another piece of information. A ground station receiving from several such satellites and wishing to avoid dataExpand
  • 153
  • 19
  • Open Access
Concept Learning and Heuristic Classification in WeakTtheory Domains
Abstract This paper describes a successful approach to concept learning for heuristic classification. Almost all current programs for this task create or use explicit, abstract generalizations. TheseExpand
  • 319
  • 15
  • Open Access
Explicitly representing expected cost: an alternative to ROC representation
ABSTRACT This paper proposes an alternative to ROC representation, in which the expected cost of a classi er is represented explicitly. This expected cost representation maintains many of theExpand
  • 187
  • 13
  • Open Access
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
This paper investigates how the splitting criteria and pruning methods of decision tree learning algorithms are influenced by misclassification costs or changes to the class distribution. SplittingExpand
  • 203
  • 12
  • Open Access