On learning dual classifiers for better data classification

- A dual classification (DuC) approach is presented to deal with the potential drawback of instance selection.
- During training, two classifiers are trained by a 'good' and 'noisy' sets respectively after performing instance selection.
- Experiments are conducted used 50 small scale and 4 large scale datasets.
- The results show that the DuC approach outperforms three state-of-the-art instance selection algorithms.