Name
Title | ||
|---|---|---|
Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification |
Abstract | ||
|---|---|---|
Abstract. Using a new method published by the first author, this chapter shows,how ,knowledge ,in the ,form ,of a ranked ,data relationship and an induced rule can be directly ,extracted from each training case for a Multilayer Perceptron (MLP) network ,with ,binary ,inputs. The knowledge extracted from all training cases can be used to validate the MLP network and the ranked ,data ,relationship for any input ,case ,provides ,direct user explanations. The method,is demonstrated for,example,training cases from a real-world MLP that classifies low back pain patients into three diagnostic classes. In using the method ,to validate the ,network ,a number ,of test cases apparently mis-classified by the network were found to have most likely been incorrectly classified by the ,clinicians. The method ,uses a direct ,approach which does not depend,on combinatorialsear ch and is thus applicable to realworld networks with large numbers of input features, as demonstrated in this current study. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/10719871_19 | Hybrid Neural Systems |
Keywords | DocType | ISBN |
direct explanations,performs low,pain classification,knowledge extraction,multilayer perceptron network,multilayer perceptron | Conference | 3-540-67305-9 |
Citations | PageRank | References |
6 | 0.95 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marylin L. Vaughn | 1 | 9 | 3.13 |
| Steven J. Cavill | 2 | 9 | 2.79 |
| Stewart J. Taylor | 3 | 6 | 0.95 |
| Michael A. Foy | 4 | 8 | 2.40 |
| B. J. Fogg | 5 | 2694 | 515.36 |