Using table lens to interactively build classifiers

Rather than induce classification rules by sophisticated algorithms, we introduce a fully interactive approach for building classifiers from large multivariate datasets based on the table lens, a multidimensional visualization technique, and appropriate interaction capabilities. Constructing classifiers is an interaction with a feedback loop. The domain knowledge and human perception can be profitably included. In our approach, both continuous and categorical attributes are processed uniformly, and continuous attributes are partitioned on the fly. Our performance evaluation with data sets from the UCI repository demonstrates that this interactive approach is useful to easily build understandable classifiers with high prediction accuracy and no required a prior knowledge about the datasets.