Learn More
Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN's performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by(More)
Indexing of cases is an important topic for MemoryBased Reasoning(MBR). One key problem is how assign weights to attributes of cases. Although several weighting methods have been proposed, some methods cannot handle numeric attributes directly, so it is necessary to discretize numeric values by classification. Furthermore, existing methods have no(More)
In mammals, behavioral and physiologic processes display 24-hour rhythms that are regulated by a circadian system consisting of central and peripheral oscillators. Because various cardiovascular functions show diurnal variations and abnormal patterns of circadian blood pressure variation carry a high risk of cardiovascular complications, we investigated(More)
A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms. The Minimum Risk Metric does not require any local restriction, its performances are comparable to the Bayes Classiier, its analytical form is simple and well founded, and nally, equipped with a simple Naive Estimator, outper-forms the other metrics. The(More)
  • 1