Data Mining: Practical Machine Learning Tools and ... - LIDeCC

414 CHAPTER 10 | THE EXPLORERLBR (for Lazy Bayesian Rules) is a Bayesian classifier that defers all processingto classification time. For each test instance it selects a set of attributes forwhich the independence assumption should not be made; the others are treatedas independent of each other given the class and the selected set of attributes.It works well for small test sets (Zheng and Webb 2000).LWL is a general algorithm for locally weighted learning. It assigns weightsusing an instance-based method and builds a classifier from the weightedinstances. The classifier is selected in LWL’s object editor: a good choice is NaïveBayes for classification problems and linear regression for regression problems(Section 6.5, pages 251–253). You can set the number of neighbors used, whichdetermines the kernel bandwidth, and the kernel shape to use for weighting—linear, inverse, or Gaussian. Attribute normalization is turned on by default.Miscellaneous classifiersThe misc. category includes two simple classifiers that were mentioned at theend of Section 4.7 (page 136). Hyperpipes, for discrete classification problems,records the range of values observed in the training data for each attribute andcategory and works out which ranges contain the attribute values of a test instance,choosing the category with the largest number of correct ranges. VFI(voting feature intervals) constructs intervals around each class by discretizingnumeric attributes and using point intervals for nominal ones, records classcounts for each interval on each attribute, and classifies test instances by voting(Demiroz and Guvenir 1997). A simple attribute weighting scheme assignshigher weight to more confident intervals, where confidence is a function ofentropy. VFI is faster than Naïve Bayes but slower than hyperpipes. Neithermethod can handle missing values.10.5 Metalearning algorithmsMetalearning algorithms, listed in Table 10.6, take classifiers and turn them intomore powerful learners. One parameter specifies the base classifier; othersspecify the number of iterations for schemes such as bagging and boosting andan initial seed for the random number generator. We already met FilteredClassifierin Section 10.3: it runs a classifier on data that has been passed through afilter, which is a parameter. The filter’s own parameters are based exclusively onthe training data, which is the appropriate way to apply a supervised filter totest data.Bagging and randomizationBagging bags a classifier to reduce variance (Section 7.5, page 316). This implementationworks for both classification and regression, depending on the base