Name
Abstract | ||
|---|---|---|
In practice, new data are constantly generated, and the existing data complexity algorithms are based on the idea of batch learning. In the face of the dynamic increase of data scale, how to measure the characteristic information of data has become an urgent problem to be solved in the field of data mining. This paper focuses on this problem and further studies its incremental learning function on the basis of in-depth discussion of data complexity proposed by TK Ho et al. Among them, N3 and N4 are the complexity indexes based on KNN classifier (K=1). In this paper, the incremental learning algorithm I1NN was proposed on the basis of 1-NN classifier, and its feasibility and validity were verified on both the artificial data set and the UCI public data set. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CcS49175.2020.9231514 | 2020 International Symposium on Community-centric Systems (CcS) |
Keywords | DocType | ISBN |
Data complexity,Incremental learning,KNN | Conference | 978-1-7281-8742-6 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||