Name
Title | ||
|---|---|---|
FleBiC: Learning classifiers from high-dimensional biomedical data using discriminative biclusters with non-constant patterns |
Abstract | ||
|---|---|---|
•Discriminative non-constant patterns (additive, multiplicative, order-preserving) aid high-dimensional data classification.•In biomedicine, non-constant assumptions handle variable individual biophysiology, disease morphology and progression stage.•Biclustering large sets of noise-tolerant discriminative patterns minimizes match scarcity, emulating boosting principles.•Coherence-sensitive pattern scoring (training) and matching (testing) improve associative classification.•FleBiC offers a way out of generalization difficulties, places statistical guarantees, and promotes interpretability. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.107900 | Pattern Recognition |
Keywords | DocType | Volume |
Associative classification,Discriminative paterns,Biclustering,Non-constant patterns,Biomedical data,High-dimensional data | Journal | 115 |
Issue | ISSN | Citations |
1 | 0031-3203 | 2 |
PageRank | References | Authors |
0.37 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Henriques | 1 | 143 | 12.35 |
| Sara C. Madeira | 2 | 1242 | 66.91 |