Name
Abstract | ||
|---|---|---|
The instance selection based model is an effective multiple-instance learning (MIL) framework, which solves the MIL problems by embedding examples (bags of instances) into a new feature space formed by some concepts (represented by some selected instances). Most previous studies use single-point concepts for the instance selection, where every possible concept is represented by only a single instance. In this paper, we apply multiple-point concepts for choosing instances, in which each possible concept is jointly represented by a group of similar instances. Furthermore, we establish an iterative instance selection based MIL framework based on multiple-point concepts, which is guaranteed to automatically converge to the needed number of concepts for a given problem. The experimental results demonstrate that the proposed framework can better handle not only common MIL problems but also hybrid ones compared to state-of-the-art MIL algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICTAI.2018.00121 | 2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) |
Keywords | Field | DocType |
multiple-instance learning, instance selection, multiple-point concept, iterative learning framework, hybrid assumption | Feature vector,Embedding,Computer science,Iterative method,Supervised learning,Redundancy (engineering),Artificial intelligence,Instance selection,Machine learning | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liming Yuan | 1 | 4 | 3.09 |
| Xian-Bin Wen | 2 | 55 | 16.67 |
| Lu Zhao | 3 | 63 | 15.63 |
| Haixia Xu | 4 | 17 | 1.18 |