Browsing by Author "Babatunde, R.S."
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Item Comparative Approach of Back-Propagation Neural Network and Decision Tree on Breast Cancer Classification: An Appraisal(Dept. of Computer Science, LAUTECH Ogbomoso, 2019) Babatunde, R.S.; Adewole, K.S.; Ajiboye, A.R.The use of data mining methods in incorporating decision making has been increasing in the past decades. Data mining simply refers to extracting or mining knowledge from large amount of data. Over the years, medical image processing has benefited immensely from data mining techniques including breast cancer diagnosis. Sonography (also known as ultrasound) has become a great addition to mammography and magnetic resonance imaging (MRI) as imaging techniques dedicated to providing breast cancer screening. This technique is time-consuming and often characterized with low accuracy. Hence, the need to develop a robust classification model with high performance accuracy and reduced false alarm. In this paper, the performance of back propagation neural networks (BPNN) and C4.5 decision tree (DT) for breast cancer prediction was carried out. Filter based feature selection approach using correlation filter was employed for ranking features according to their predictive power. The model was simulated using WEKA data mining tools and extensive comparative study was performed based on the standard evaluation metrics. The performance of the two classifiers was compared based on their predictive accuracy, precision, recall, kappa statistic and other relevant statistical measures. The simulation results shows that C4.5 outperforms BPNN in terms of training time (0.16 secs) and accuracy (94.2857% ) while BPNN has 46.9secs training time and accuracy of 90.9524%. However, the result also reveals that BPNN outperforms C4.5 in terms of error rate, with BPNN having mean absolute error of 0.0542 while C4.5 has mean absolute error of 0.0834. It can therefore be deduced from the comparison that C4.5 can be a good option for prediction task considering the fast training time of the algorithm as well as the high accuracy of prediction.Item A NOVEL APPROACH TO OUTLIERS REMOVAL IN A NOISY NUMERIC DATA SET FOR EFFICIENT MINING(Department of Computer Science, University of Ilorin., 2016) Ajiboye, A.R.; Adewole, K.S.; Babatunde, R.S.; Oladipo, I.D.Data pre-processing is a key task in the data mining process. The task generally consumes the largest portion of the total data engineering effort while unveiling useful patterns from datasets. Basically, data mining is about fitting descriptive or predictive models from data. However, the presence of outlier sometimes reduces the reliability of the models created. It is, therefore, essential to have raw data properly pre-processed before exploring them for mining. In this paper, an algorithm that detects and removes outliers in a numeric dataset is proposed. In order to establish the effectiveness of the proposed algorithm, the clean data obtained through the implementation of the proposed approach is used to create a prediction model. Similarly, the clean data obtained through the use of one of the existing techniques is also used to create a prediction model. Each of the models created is simulated using a set of untrained data and the error associated with each model is measured. The resulting outputs from the two approaches reveal that, the prediction model created using the output from the proposed algorithm has an error of 0.38, while the prediction model created using the cleaned data from the clustering method gives an error of 0.61. Comparison of the errors associated with the models created using the two approaches shows that, the proposed algorithm is suitable for cleaning numeric dataset. The results of the experiment also unveils that, the proposed approach is efficient and can be used as an alternative technique to other existing cleaning methods.