Comparative Study of Instance Based Learning and Back Propagation for Classification Problems

Scientific Study from the year 2008 in the subject Computer Science - Applied, grade: 95, University of Essex (Department of Computer Science), course: Machine Learning, language: English, abstract: The paper presents a comparative study of the performance of Back Propagation and Instance Based Learning Algorithm for classification tasks. The study is carried out by a series of experiments with all possible combinations of parameter values for the algorithms under evaluation. The algorithm's classification accuracy is compared over range of datasets and measurements like Cross Validation, Kappa Statistics, Root Mean Squared Value and True Positive vs False Positive rate have been used to evaluate their performance. Along with performance comparison, techniques of handling missing values have also been compared that include Mean/Mode replacement and Multiple Imputation.