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In this paper, we have presented some results on undergraduate student retention using signal processing techniques for classification of the student data. The experiments revealed that the main factor that influences student retention in the Historically Black Colleges and Universities (HBCU) is the cumulative grade point average (GPA). The linear smoothing of the data helped remove the noise spikes in data thereby improving the retention results. The data is decomposed into Haar coefficients that helped accurate classification. The results showed that the HBCU undergraduate student retention corresponds to an average GPA of 2.8597 and the difference of −0.023307. Using this approach we obtained more accurate retention results on training data.

In this paper, we have presented some results of undergraduate student retention using machine learning and wavelet decomposition algorithms for classifying the student data. We have also made some improvements to the classification algorithms such as Decision tree, Support Vector Machines (SVM), and neural networks supported by Weka software toolkit. The experiments revealed that the main factors that influence student retention in the Historically Black Colleges and Universities (HBCU) are the cumulative grade point average (GPA) and total credit hours (TCH) taken. The target functions derived from the bare minimum decision tree and SVM algorithms were further revised to create a two-layer neural network and a regression to predict the retention. These new models improved the classification accuracy. Furthermore, we utilized wavelet decomposition and achieved better results.