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Three-Phase Tournament-Based Method for Better Email Classification

Email classification performancehas attracted much attention in the last decades.This paper proposes a tournament-based method to evolve email classification performance utilizing World Final Cup rules as a solution heuristics. Our proposed classification method passes through three phases: 1) clustering (grouping) email folders (topics or classes) based on their tokenand field similarities, 2) training binary classifiers on each class pairand 3) applying 2-layer tournament method for the classifiers of the related classes in the resultant clusters.The first phase evolves K-mean algorithm to result in cluster sizes of 3, 4, or 5 email classes with the pairwise similarityfunction. The second phase uses two classifiers namely Maximum Entropy (MaxEnt) and Winnow. The third phase uses a 2-layer tournament method which applies round robin and elimination tournament methods sequentially to realize the winner class per cluster and the winner of all clusters respectively. The proposed method is tested for various K settings against tournament and N-way methods using 10-fold cross-validation evaluation method on Enron benchmark dataset. The experiments prove that the proposed method is generally more accurate than the others.