The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy ...
We consider three natural models of random logarithmic depth decision trees over Boolean variables. We give an efficient algorithm that for each of these models learns all but an ...
Classification learning has been dominated by the induction of axisorthogonal decision surfaces. While induction of alternate forms of decision surface has received some attentio...
In this paper, we investigate privacy-preserving data imputation on distributed databases. We present a privacypreserving protocol for filling in missing values using a lazy deci...