Decision lists and decision trees are two models of computation for boolean functions. that rank-k decision trees are a subclass of decision lists. Here we identify precisely, by giving a syntactical characterization, the subclass of decision lists which correspond exactly to the class of bounded rank decision trees. Furthermore we give a more general algorithm to recover reduced decision trees...

Administrative: • Enrollment should be good now. • Next homework will go out next Wednesday or Friday. • Recap of where we are in course, and interplay between representation, optimization, and generalization; usual story is that as representation power improves, optimization and generalization become more painful. • Many basic latex errors in assignments; please consult lshort for an easy refe...

In this paper we use the methodology introduced in Dhurandhar and Dobra (2006) for analyzing the error of classifiers and the model selection measures, to analyze decision tree algorithms. The methodology consists of obtaining parametric expressions for the moments of the Generalization error (GE) for the classification model of interest, followed by plotting these expressions for interpritabil...

Decision trees and their ensembles are very popular models of supervised machine learning. In this paper we merge the ideas underlying decision trees, FCA by proposing a new learning model which can be constructed in polynomial time is applicable for both classification regression problems. Specifically, first propose polynomial-time algorithm constructing part concept lattice that based on tre...

conclusions: absence of the c allele was significantly associated with failure of response. the presence of c allele was associated with a favorable outcome. afp is a strong baseline predictor of hcv treatment response. a decision tree model is useful for predicting the probability of response to therapy. patients and methods: the study included 263 chronic hcv egyptian patients receiving peg-i...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Clas...

For the well-known concept of decision trees as it is used for inductive inference we study the natural concept of equivalence: two decision trees are equivalent if and only if they represent the same hypothesis. We present a simple e cient algorithm to establish whether two decision trees are equivalent or not. The complexity of this algorithm is bounded by the product of the sizes of both dec...

We describe an experimental study of Option Decision Trees with majority votes. Option Decision Trees generalize regular decision trees by allowing option nodes in addition to decision nodes; such nodes allow for several possible tests to be conducted instead of the commonly used single test. Our goal was to explore when option nodes are most useful and to control the growth of the trees so tha...