Label-noise reduction with support vector machines

This dataset reports data generated through the investigation of the problem of detection of label-noise in large pattern-recognition datasets. Specifically, results of label-noise reduction on two datasets are reported. The University of California, Irvine (UCI) Letter Recognition dataset and the Mixed National Institute of Standards and Technology (MNIST) Digit Recognition dataset was used to train an algorithm. The algorithm was then tested a Plankton dataset collected by the SIPPER (Shadow Imaging Particle Profiler and Evaluation Recorder) camera imaging system during trips to the site of the Deepwater Horizon Oil Spill. The experiment with the Plankton dataset represented a more practical application of data cleansing, because the label-noise naturally occurred in the data. Data presented in this database include the noise detected for four classes, total noise detected, and the percent accumulative noise detected for both the UCI and MNIST datasets. SIPPER data are not reported in this dataset. SIPPER data can be found in dataset R1.x130.000:0002 "Application of image processing and machine learning techniques to distinguish suspected oil droplets from plankton and other particles for the SIPPER imaging system". On a dataset that contained images of plankton with inadvertent noise, the new algorithm was able to detect all incorrect samples in the class of interest by reviewing only 5% of the data. Thus, the described approach helps to significantly reduce the effort needed to remove label-noise from data. Data published in: Felatyev, S., M. Shreve, K. Kramer, L. Hall, D. Goldgof, R. Kasturi, K. Daly, A. Remsen, H. Bunke. 2012. Label-Noise Reduction with Support Vector Machines. International Conference on Pattern Recognition (ICPR), November 2012, Tsukuba Science City, Japan.