Separate, Measure and Conquer

Separate-and-conquer Regression

In this paper a rule learning algorithm for the prediction of numerical target variables is presented. It is based on the separate-and-conquer strategy and the classification phase is done by a decision list. A new splitpoint generation method is introduced for the efficient handling of numerical attributes. It is shown that the algorithm performs comparable to other regression algorithms where...

Separate, Measure and Conquer: Faster Algorithms for Max 2-CSP and Counting Dominating Sets

We show a method resulting in the improvement of several polynomial-space, exponentialtime algorithms. An instance of the problem Max (r, 2)-CSP, or simply Max 2-CSP, is parametrized by the domain size r (often 2), the number of variables n (vertices in the constraint graph G), and the number of constraints m (edges in G). When G is cubic, and omitting sub-exponential terms here for clarity, we...

Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction

Censoring weighted separate-and-conquer rule induction from survival data.

OBJECTIVES Rule induction is one of the major methods of machine learning. Rule-based models can be easily read and interpreted by humans, that makes them particularly useful in survival studies as they can help clinicians to better understand analysed data and make informed decisions about patient treatment. Although of such usefulness, there is still a little research on rule learning in surv...

Measure and Conquer: Domination - A Case Study

Davis-Putnam-style exponential-time backtracking algorithms are the most common algorithms used for finding exact solutions of NP-hard problems. The analysis of such recursive algorithms is based on the bounded search tree technique: a measure of the size of the subproblems is defined; this measure is used to lower bound the progress made by the algorithm at each branching step. For the last 30...