Browsing by Author "Salawu, M.D."

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    A Chi-Square-SVM Based Pedagogical Rule Extraction Method for Microarray Data Analysis
    (Institute of Advanced Engineering and Science - International Journal of Advances in Applied Sciences. 9(2): 93 – 100, 2020) Salawu, M.D.; Arowolo, M.O.; Abdulsalam, S.O.; Isiaka, R.M.; Jimada-Ojuolape, B.; Mudashiru, L.O.; Gbolagade, K.A.
    Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms. Keywords: Machine learning, Medical diagnosis, Rule-extraction, SVMs
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    A Feature Selection Based on One-Way ANOVA for Microarray Data Classification
    (College of Natural Sciences, Al-Hikmah University, Ilorin, Nigeria - Al-Hikmah Journal of Pure and Applied Sciences, 2016) Arowolo, M.O.; Abdulsalam, S.O.; Saheed, Y.K.; Salawu, M.D.
    High dimensionality of microarray data and expressions of thousands of features in a much smaller number of samples is a challenge affecting the applicability of the analytical results. However Support Vector Machine (SVM) has been commonly used in the classification of microarray datasets, yet the problem of high dimensionality of the feature space of data still exist. This study deals with the reduction of gene expression data into a minimal subset of genes, by introducing feature selection, to greatly reduce computational burden and noise arising from irrelevant genes that can perform a classification of cancer from microarray data using machine learning. Various statistical theory and Machine Learning (ML) algorithms to select important features, remove redundant and irrelevant features have been proposed, but it is unclear how these algorithms respond to conditions like small sample-sizes. This paper presents combination of Analysis of Variance (ANOVA) for feature selection; to reduce high data dimensionality of feature space and SVM algorithms technique for classification; to reduce computational complexity and effectiveness. Computational burden and noise arising from redundant and irrelevant features are eliminated. It reduces gene expression data to a lesser number of genes rather than thousands of genes, which can drop the cost for cancer testing significantly. The proposed approach selects most informative subset of features for classification to obtain a high performance accuracy, sensitivity, specificity and precision. Key words: Gene expressions, Microarray, One-Way-ANOVA, Support Vector Machines