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Post Classification Label Refinement Using Implicit Ordering Constraint Among Data Instances

Classification of instances into different categories in various real world applications suffer from inaccuracies due to lack of representative training data, limitations of classification models, noise and outliers in the input data etc. In this paper we propose a new post classification label refinement method for the scenarios where data instances have an inherent ordering among them that can be leveraged to correct inconsistencies in class labels. We show that by using the ordering constraint, more robust algorithms can be developed than traditional methods. Moreover in most applications where this ordering among instances exists, it is not directly observed. The proposed approach simultaneously estimates the latent ordering among instances and corrects the class labels. We demonstrate the utility of the approach for the application of monitoring the dynamics of lakes and reservoirs. The proposed approach has been evaluated on synthetic datasets with different noise structures and noise levels.