Application of Ranking Based Attribute Selection Filters to Perform Automated Evaluation of Descriptive Answers through Sequential Minimal Optimization Models

In this paper, we study the performance of various models for automated evaluation of descriptive answers by using rank based feature selection filters for dimensionality reduction. We quantitatively analyze the best from amongst five rank based feature selection techniques, namely Chi Squared filter, Information Gain filter, Gain Ratio filter, Relief filter and Symmetrical Uncertainty filter. We use Sequential Minimal Optimization with Polynomial kernel to build models and we evaluate these models in terms of various parameters such as Accuracy, Time to build the models, Kappa, Mean Absolute Error and Root Mean Squared Error. For all except the Relief filter, the accuracies obtained are at least 4% better than the accuracies obtained with the same models without any filters applied. We found that the accuracies recorded are same for Chi Squared filter, Information Gain filter, Gain Ratio filter and Symmetrical Uncertainty filter. Therefore accuracy alone is not the sole determinant in selecting the best filter. The time taken to build the models, Kappa, Mean Absolute Error and Root Mean Squared Error play a major role in determining the effectiveness of these filters. The overall rank aggregation metric of Symmetrical Uncertainty filter is 45 and this is better by 1 rank than the rank aggregation metric of Information gain filter. Symmetric Uncertainty filter's rank aggregation metric is better by 3, 6, 112 ranks respectively when compared to the rank aggregation metrics of Chi Squared filter, Gain Ratio filter and Relief filters. Through these quantitative measurements, we conclude that Symmetrical Uncertainty attribute evaluation is the overall best performing rank based feature selection algorithm applicable for auto evaluation of descriptive answers.