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Comparative Analysis of Classification Algorithms on Endometrial Cancer Data
Objective: To expose the Performance of classification algorithms on endometrial cancer data. The best algorithms are listed based on the result of various test options and ranked based on their accuracies. Methods and Analysis: Classification is one of the data mining techniques used to find a model that describes the data classes or concepts. The class-label of strange instance is predicted with the help of classification. It compares the classification algorithms by measuring accuracies, speed and strength of algorithms using WEKA tool. Accuracies of classification algorithms are calculated by means of four different options. The error rate and time taken to build the model also measured. Findings: The accuracies of sixteen algorithms are measured by training set, test set, tenfold cross validation and percentage split testing options. The average accuracies are calculated, then compared and ranked with highest accuracy first. The best five algorithms are taken for final performance on endometrial cancer dataset. The accuracy of Random Forest algorithm is high, but it took 0.16 sec to build the model, whereas the IBK, Random Tree and KStar algorithms’ performs well with 0sec to build the model. Bagging algorithm takes more time to build the model. In terms of time and accuracy IBK produces better results as compared to other algorithms. Random Forest algorithm is most excellent in provisos of correctly classified occurrence. Novelty/Improvement: With the 315 instances of endometrial cancer data, the time taken to build the model is zero for IBK, KStar and Random Tree algorithms. If the number of instance increases then time also will increase.
Classification Algorithms, Endometrial Cancer, IBK, KStar, Random Tree.
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