Abstract
The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even more in the context of increasing number of different classifiers applied in toxicity prediction. Consequently, there exist a need to develop tools to integrate various approaches. The goal of this research is to apply neuro-fuzzy networks to provide an improvement in combining the results of five classifiers applied in toxicity of pesticides. Nevertheless, fuzzy rules extracted from the trained developed networks can be used to perform useful comparisons between the performances of the involved classifiers. Our results suggest that the neuro-fuzzy approach of combining classifiers has the potential to significantly improve common classification methods for the use in toxicity of pesticides characterization, and knowledge discovery.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 293-303 |
| Number of pages | 11 |
| Volume | 2364 |
| ISBN (Print) | 3540438181, 9783540438182 |
| Publication status | Published - 2002 |
| Event | 3rd International Workshop on Multiple Classifier Systems, MCS 2002 - Cagliari, Italy Duration: Jun 24 2002 → Jun 26 2002 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2364 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 3rd International Workshop on Multiple Classifier Systems, MCS 2002 |
|---|---|
| Country | Italy |
| City | Cagliari |
| Period | 6/24/02 → 6/26/02 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Combining classifiers of pesticides toxicity through a neuro-fuzzy approach. / Benfenati, Emilio; Mazzatorta, Paolo; Neagu, Daniel; Gini, Giuseppina.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2364 Springer Verlag, 2002. p. 293-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2364).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Combining classifiers of pesticides toxicity through a neuro-fuzzy approach
AU - Benfenati, Emilio
AU - Mazzatorta, Paolo
AU - Neagu, Daniel
AU - Gini, Giuseppina
PY - 2002
Y1 - 2002
N2 - The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even more in the context of increasing number of different classifiers applied in toxicity prediction. Consequently, there exist a need to develop tools to integrate various approaches. The goal of this research is to apply neuro-fuzzy networks to provide an improvement in combining the results of five classifiers applied in toxicity of pesticides. Nevertheless, fuzzy rules extracted from the trained developed networks can be used to perform useful comparisons between the performances of the involved classifiers. Our results suggest that the neuro-fuzzy approach of combining classifiers has the potential to significantly improve common classification methods for the use in toxicity of pesticides characterization, and knowledge discovery.
AB - The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even more in the context of increasing number of different classifiers applied in toxicity prediction. Consequently, there exist a need to develop tools to integrate various approaches. The goal of this research is to apply neuro-fuzzy networks to provide an improvement in combining the results of five classifiers applied in toxicity of pesticides. Nevertheless, fuzzy rules extracted from the trained developed networks can be used to perform useful comparisons between the performances of the involved classifiers. Our results suggest that the neuro-fuzzy approach of combining classifiers has the potential to significantly improve common classification methods for the use in toxicity of pesticides characterization, and knowledge discovery.
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UR - http://www.scopus.com/inward/citedby.url?scp=84947568641&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84947568641
SN - 3540438181
SN - 9783540438182
VL - 2364
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 303
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -