Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | Proceedings of Artifical Intelligence in Medicine Europe (AIME) |
| Editors | R Bellazzi, A Abu-Hanna, J Hunter |
| Place of Publication | Berlin |
| Publisher | Springer |
| Pages | 316-326 |
| Number of pages | 11 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 11th Conference on Artificial Intelligence in Medicine, AIME 2007 - Amsterdam, Netherlands Duration: 7 Jul 2007 → 11 Jul 2007 Conference number: 11 |
Publication series
| Name | Lecture Notes in Artificial Intelligence |
|---|---|
| Publisher | Springer Verlag |
| Number | LNCS4549 |
| Volume | 4594 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 11th Conference on Artificial Intelligence in Medicine, AIME 2007 |
|---|---|
| Abbreviated title | AIME |
| Country | Netherlands |
| City | Amsterdam |
| Period | 7/07/07 → 11/07/07 |
Keywords
- EWI-10747
- METIS-241770
- IR-61840
- misclassification error
- Shannon entropy
- Test selection
- Gini index
Cite this
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On the Behaviour of Information Measures for Test Selection. / Sent, D.; van der Gaag, L.C.
Proceedings of Artifical Intelligence in Medicine Europe (AIME). ed. / R Bellazzi; A Abu-Hanna; J Hunter. Berlin : Springer, 2007. p. 316-326 (Lecture Notes in Artificial Intelligence; Vol. 4594, No. LNCS4549).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - On the Behaviour of Information Measures for Test Selection
AU - Sent, D.
AU - van der Gaag, L.C.
PY - 2007
Y1 - 2007
N2 - tests that are expected to yield the largest decrease in the uncertainty about a patient’s diagnosis. For capturing diagnostic uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that for a large range of values, the first derivative of the Gini index can be regarded as an approximation of the first derivative of the Shannon entropy. We also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. We further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.
AB - tests that are expected to yield the largest decrease in the uncertainty about a patient’s diagnosis. For capturing diagnostic uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that for a large range of values, the first derivative of the Gini index can be regarded as an approximation of the first derivative of the Shannon entropy. We also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. We further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.
KW - EWI-10747
KW - METIS-241770
KW - IR-61840
KW - misclassification error
KW - Shannon entropy
KW - Test selection
KW - Gini index
U2 - 10.1007/978-3-540-73599-1_42
DO - 10.1007/978-3-540-73599-1_42
M3 - Conference contribution
T3 - Lecture Notes in Artificial Intelligence
SP - 316
EP - 326
BT - Proceedings of Artifical Intelligence in Medicine Europe (AIME)
A2 - Bellazzi, R
A2 - Abu-Hanna, A
A2 - Hunter, J
PB - Springer
CY - Berlin
ER -