TSSweb: A web tool for training set selection

Supervised learning methods aimed at performing precise predictions by learning from labeled training data. Unfortunately, training data can contain noisy or wrong information, specially when they come from real-world applications. In this scenario, applying a so-called training set selection procedure on data can lead to improve the performance of the supervised learning methods used for classification or regression tasks. In literature, several training set selection techniques have been proposed, but, to the best of our knowledge, few software tools implement this procedure. Moreover, all of them require programming capabilities or software package installation what makes their use difficult for people without specific computer skills. This paper proposes the first web-based tool, named TSSweb, for performing an accurate selection of the training instances. Thanks to its web nature, TSSweb enables all researchers, coming from several and heterogeneous scientific backgrounds, to reduce own datasets so as to improve their analysis and reduce the execution time of their supervised learning models. As shown in the experimental session, TSSweb produces reduced datasets with a good quality as well as being user-friendly.