A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all

Discretization of continuous attributes is an important task for certain types of machine learning algorithms. Bayesian approaches, for instance, require assumptions about data distributions. Decision Trees, on the other hand, require sorting operations to deal with continuous attributes, which

Discretization of continuous attributes into ordered discrete attributes can be beneficial even for propositional induction algorithms that are capable of handling continuous attributes directly. Benefits include possibly large improvements in induction time, smaller sizes of induced trees or rule

. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. 1

Abstract Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous

Abstract. Real-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attributes. Methods

for the continuous valued attributes commonly found in scientific and statistical databases. We propose two methods based on spline functions for estimating the selectivity of range queries over univariate and multivariate data. These methods are more accurate than histograms. As the results from our experiments

Abstract — Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of Ant Colony Optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly

Several parametric representations of the acoustic signal were compared as to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary in-cluded many phonetically similar monosyllabic words, therefore the emphasis was on ability to retain

We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new metric based on the Minimal Description Length principle for choosing the threshold values