Name
Abstract | ||
|---|---|---|
Learning from examples is a frequently arising challenge, with a large number of algorithms proposed in the classification,
data mining and machine learning literature. The evaluation of the quality of such algorithms is frequently carried out ex post, on an experimental basis: their performance is measured either by cross validation on benchmark data sets, or by clinical
trials. Few of these approaches evaluate the learning process ex ante, on its own merits. In this paper, we discuss a property of rule-based classifiers which we call “justifiability”, and which
focuses on the type of information extracted from the given training set in order to classify new observations. We investigate
some interesting mathematical properties of justifiable classifiers. In particular, we establish the existence of justifiable
classifiers, and we show that several well-known learning approaches, such as decision trees or nearest neighbor based methods,
automatically provide justifiable classifiers. We also identify maximal subsets of observations which must be classified in
the same way by every justifiable classifiers. Finally, we illustrate by a numerical example that using classifiers based
on “most justifiable” rules does not seem to lead to overfitting, even though it involves an element of optimization. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10479-011-0916-1 | Annals of Operations Research |
Keywords | DocType | Volume |
Decision Tree,Boolean Function,Disjunctive Normal Form,Elementary Conjunction,Maximal Theory | Journal | 188 |
Issue | ISSN | Citations |
1 | 0254-5330 | 8 |
PageRank | References | Authors |
0.50 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Endre Boros | 1 | 1779 | 155.63 |
| Yves Crama | 2 | 547 | 63.94 |
| Peter L. Hammer | 3 | 1996 | 288.93 |
| Toshihide Ibaraki | 4 | 2593 | 385.64 |
| A. Kogan | 5 | 142 | 12.92 |
| Kazuhisa Makino | 6 | 1088 | 102.74 |