Version 1.3
Code C++ on HP workstation
References ILP97
Pointers http://www.lri.fr/ia/sebag/introduction.en.html
Full name Distance Induction with STILL

DISTILL aims at mapping the problem domain onto a metric space, because a number of robust data analysis algorithms are applicable into metric spaces, to various aims (discrimination, regression, clustering, visualization,...).

One first remarks that a set of disjunctive hypotheses defines a mapping from the problem domain onto the space of integer vectors . Let h be given as , where are conjunctive hypotheses in . Let <E,h> denote the number of which subsume E. The mapping from the problem domain onto is defined as follows:

By the way, this mapping onto the metric space automatically defines a metric structure on :

Expectedly, the relevance of this metric structure increases with the number p of hypotheses hi considered, and the diversity of these hypotheses. This relevance will be empirically estimated from the predictive accuracy of a k-nearest neighbor classification process based on the above distance.

Hypotheses are termed dimensions of the mapping , and p is the user-supplied number of dimensions. DISTILL uses fragments of hypotheses built by STILL (see) as dimensions of : p pairs of examples (, ) are randomly selected; hypothesis is set to the disjunctive hypothesis D(, ), which covers and discriminates . Thanks to the stochastic subsumption mechanism of STILL, can be approximately characterized and used with linear complexity in the size of the examples. DISTILL complexity (computing the distance of any two examples) finally is

where p is the number of dimensions, the number of samples considered during induction, K the number of samples considered during classification, and p the maximum number of literals of an example.

DISTILL involves one parameter less-than STILL: parameters and M, which control the consistency and generality requirements in STILL, are replaced by the number of dimensions p.

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