Cascaded SOM: An Improved Technique for Automatic Email Classification
Naveen Kumar Saini, Sriparna Saha, Pushpak Bhattacharyya · 2018
The self-organizing map (SOM) helps in exploratory phase of data mining by projecting the input data into a lower dimensional map. In recent years SOM has also been applied for classification of data points. The prominent utility of SOM based classification is evident from the use of no labeled data during training. In a multi-class classification problem where classes have high degree of overlap, it would be difficult to design a single-level SOM based classification system which can perform well for all the classes. In order to deal with the multi-class classification efficiently, the current paper proposes to develop a Cascaded SOM based architecture where classes are handled in a hierarchical way. Also, it can be applied for solving any multi-class classification problem where labeled data is limited. As a case study, in the first part of the paper results are shown for single label version of complex email classification problem where classes are highly overlapping to each other and in the second part, results are shown for some multi-labeled data sets. Different representation schemas for emails and a large set of features are also adopted for the purpose of experiment. Proposed Cascaded SOM based classification model performs well in email-classification compared to standard classification approaches and classical SOM based model.