A new density core graph-cut class decomposition to improve neural network classification performance

Abstract

This research presents a new pre-processed class decomposition technique called density core graph-cut (DCGC). The method uses supervised clustering instead of a traditional unsupervised one to decompose the class. Supervised clustering requires additional label information to function and with that it gains a better understanding of the distribution. DCGC employs nearest neighbors to form a density core graph for each class. Then, the edges of each graph to be removed or cut is identified utilizing class information. Lastly, it yields final clusters by grouping all connected cores and assigning the remaining samples to a cluster where the nearest core belongs. Training neural network classifiers on complex label data will yield a better accuracy with the modified class representation. Intuitively, the decision boundaries separating classes based on the modified labels are less complex, and classifiers’ chance to reach these hyperplanes is higher. The results present that training neural networks using label representations from DCGC significantly helps improve the classification accuracy of neural networks on syntactic datasets as high as 30%. For real-world problems, the experiment presents a mixed result in which some datasets moderately benefit from DCGC.