Data weighting method on the basis of binary encoded output to solve multi-class pattern classification problems

Data weighting is of paramount importance with respect to classification performance in pattern recognition applications. In this paper, the output labels of datasets have been encoded using binary codes (numbers) and by this way provided a novel data weighting method called binary encoded output based data weighting (BEOBDW). In the proposed data weighting method, first of all, the output labels of datasets have been encoded with binary codes and then obtained two encoded output labels. Depending to these encoded outputs, the data points in datasets have been weighted using the relationships between features of datasets and two encoded output labels. To generalize the proposed data weighting method, five datasets have been used. These datasets are chain link (2 classes), two spiral (2 classes), iris (3 classes), wine (3 classes), and dermatology (6 classes). After applied BEOBDW to five datasets, the k-NN (nearest neighbor) classifier has been used to classify the weighted datasets. A set of experiments on used real world datasets demonstrated that the proposed data weighting method is a very efficient and has robust discrimination ability in the classification of datasets. BEOBDW method could be confidently used before many classification algorithms.

► A new data pre-processing method called binary encoded output based data weighting (BEOBDW) has been proposed. ► Multi-class data classification problems have been solved by means of this method. ► The proposed data weighting method is a very efficient and has robust discrimination ability in the classification of datasets.