Name
Title | ||
|---|---|---|
Dempster-Shafer Theory Based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy. |
Abstract | ||
|---|---|---|
As a pivotal task in cancer therapy, outcome prediction is the foundation for tailoring and adapting a treatment planning. In this paper, we propose to use image features extracted from PET and clinical characteristics. Considering that both information sources are imprecise or noisy, a novel prediction model based on Dempster-Shafer theory is developed. Firstly, a specific loss function with sparse regularization is designed for learning an adaptive dissimilarity metric between feature vectors of labeled patients. Through minimizing this loss function, a linear low-dimensional transformation of the input features is then achieved; meanwhile, thanks to the sparse penalty, the influence of imprecise input features can also be reduced via feature selection. Finally, the learnt dissimilarity metric is used with the Evidential K-Nearest-Neighbor (EKNN) classifier to predict the outcome. We evaluated the proposed method on two clinical data sets concerning to lung and esophageal tumors, showing good performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24574-4_83 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Outcome Prediction,PET,Feature Selection,Sparse Constraint,Dempster-Shafer Theory | Feature vector,Data set,Feature selection,Pattern recognition,Feature (computer vision),Computer science,Cancer therapy,Regularization (mathematics),Artificial intelligence,Classifier (linguistics),Dempster–Shafer theory,Machine learning | Conference |
Volume | ISSN | Citations |
9351 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunfeng Lian | 1 | 132 | 22.61 |
| Ruan Su | 2 | 559 | 53.00 |
| Thierry Denoeux | 3 | 815 | 74.98 |
| Hua Li | 4 | 45 | 9.03 |
| Pierre Vera | 5 | 59 | 10.15 |