LibD3C
| swMATH ID: | 21627 |
| Software Authors: | Lin, C.; Chen W, Q.; Qiu, C.; Wu Y, F.; Krishnan, S.; Zou, Q. |
| Description: | LibD3C: ensemble classifiers with a clustering and dynamic selection strategy. Selective ensemble is a learning paradigm that follows an “overproduce and choose” strategy, where a number of candidate classifiers are trained, and a set of several classifiers that are accurate and diverse are selected to solve a problem. In this paper, the hybrid approach called D3C is presented; this approach is a hybrid model of ensemble pruning that is based on k-means clustering and the framework of dynamic selection and circulating in combination with a sequential search method. Additionally, a multi-label D3C is derived from D3C through employing a problem transformation for multi-label classification. Empirical study shows that D3C exhibits competitive performance against other high-performance methods, and experiments in multi-label datasets verify the feasibility of multi-label D3C. |
| Homepage: | http://lab.malab.cn/soft/LibD3C/ |
| Related Software: | LIBSVM; Pse-in-One; Armadillo; FaceNet; DeepFace; VIPLFaceNet; PFRES; iNuc-STNC; iDHS-EL; ProLanGO; QAcon; iPhos-PseEn; iPPBS-Opt; iPro54-PseKNC; SMOQ; pSuc-Lys; iNuc-PseKNC; DeepQA; MEKA; PASCAL VOC |
| Cited in: | 3 Documents |
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Cited by 14 Authors
| 1 | Cangzhi, Jia |
| 1 | Chen, Cheng |
| 1 | Luo, Sheng |
| 1 | Ma, Qin |
| 1 | Miao, Duoqian |
| 1 | Qing, Yang |
| 1 | Qiu, Wenying |
| 1 | Taoying, Li |
| 1 | Tian, Baoguang |
| 1 | Wu, Xue |
| 1 | Xu, Jianfeng |
| 1 | Yu, Bin |
| 1 | Zhang, Yuanjian |
| 1 | Zhang, Zhifei |
Cited in 3 Serials
| 1 | Mathematical Biosciences |
| 1 | International Journal of Approximate Reasoning |
| 1 | Journal of Theoretical Biology |
Cited in 2 Fields
| 2 | Computer science (68-XX) |
| 2 | Biology and other natural sciences (92-XX) |
