Name
Abstract | ||
|---|---|---|
Data quality issues may bring serious problems in data analysis. For instance, missing values could decrease the accuracy of the classification. As traditional classification approaches can only be applied to complete data sets, we present a generic classification model for incomplete data where existing classification methods can be effectively incorporated. Firstly, we generate complete views from the incomplete data by choosing proper subsets of attributes based on Information Gain measure. Then we use these selected views to obtain multiple base classifiers. Finally, the base classifies are effectively combined as a final classifier with a decision tree. Extensive experiments results on real data sets demonstrate that the proposed method outperforms existing approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.asoc.2020.106437 | Applied Soft Computing |
Keywords | DocType | Volume |
Incomplete data,Missing value,Classification,Decision tree | Journal | 94 |
ISSN | Citations | PageRank |
1568-4946 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hekai Huang | 1 | 1 | 0.36 |
| Hongzhi Wang | 2 | 421 | 73.72 |
| Ming Sun | 3 | 91 | 16.25 |