Name
Title | ||
|---|---|---|
Performance weights for the linear combination data fusion method in information retrieval |
Abstract | ||
|---|---|---|
In information retrieval, the linear combination method is a very flexible and effective data fusion method, since different weights can be assigned to different component systems. However, it remains an open question which weighting schema is good. Previously, a simple weighting schema was very often used: for a system, its weight is assigned as its average performance over a group of training queries. In this paper, we investigate the weighting issue by extensive experiments. We find that, a series of power functions of average performance, which can be implemented as efficiently as the simple weighting schema, is more effective than the simple weighting schema for data fusion. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68123-6_50 | ISMIS |
Keywords | Field | DocType |
linear combination method,effective data fusion method,different component system,data fusion,information retrieval,performance weight,weighting schema,average performance,linear combination data fusion,simple weighting schema,different weight,extensive experiment,weighting issue,power function | Power function,Data mining,Linear combination,Weighting,Information retrieval,Pattern recognition,Computer science,Sensor fusion,Artificial intelligence,Schema (psychology) | Conference |
Volume | ISSN | ISBN |
4994 | 0302-9743 | 3-540-68122-1 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Wu | 1 | 370 | 33.55 |
| Qili Zhou | 2 | 5 | 1.14 |
| Yaxin Bi | 3 | 541 | 47.76 |
| Xiaoqin Zeng | 4 | 407 | 32.97 |