Name
Abstract | ||
|---|---|---|
The method of quantification were developed and investigated for the purpose of analyzing qualitative data. In the second method of quantification, the matter of interest is to discriminate the categories of the response variable. For that purpose, numerical scores of each categories are introduced so that the categories of the response variable can be discriminated as well as possible by those score. Since the total score is the sum of each category's score, the model is an additive model. Thus, if observations have a synergism, the method fails to grasp the structure. As a consequence, the response variable seems not to be discriminated by the method. In this paper, we propose an extension of Hayashi's second method of quantification by applying a fuzzy integral approach. To use the degree of decomposition of scores, we can include interactions between categories to the model. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-27774-3_20 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
additive model,qualitative data | Qualitative property,Measure (mathematics),Computer science,Artificial intelligence,Numerical analysis,Machine learning | Conference |
Volume | ISSN | Citations |
3131 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideyuki Imai | 1 | 103 | 25.08 |
| Daigo Izawa | 2 | 1 | 0.38 |
| Kiyotaka Yoshida | 3 | 1 | 0.38 |
| Yoshiharu Sato | 4 | 7 | 1.29 |