Name
Abstract | ||
|---|---|---|
Classification is an essential task in data mining, machine learning and pattern recognition areas. Conventional classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine (PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization (SMO) algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation (KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI, StatLib and mldata.org. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11432-016-9020-4 | SCIENCE CHINA Information Sciences |
Keywords | Field | DocType |
preference, SVM, classification, sampling, sequential minimal optimization (SMO) | Heuristic,Pattern recognition,Computer science,Support vector machine,Human learning,Artificial intelligence,Sampling (statistics),Knowledge base,Sequential minimal optimization,Machine learning | Journal |
Volume | Issue | ISSN |
60 | 12 | 1674-733X |
Citations | PageRank | References |
3 | 0.37 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lerong Ma | 1 | 8 | 1.51 |
| Dandan Song | 2 | 32 | 7.92 |
| Lejian Liao | 3 | 202 | 32.25 |
| Jingang Wang | 4 | 5 | 2.12 |