Browsing by Author "Gbolagade, K.A."

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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 Comparative Analysis of Feature Extraction Methods for Classifying Colon Cancer Microarray Data
    (EAI Publishing - EAI Endorsed Transactions on Scalable Information Systems, 4(14), 1 – 6, 2017) Arowolo, M.O.; Isiaka, R.M.; Abdulsalam, S.O; Saheed, Y.K.; Gbolagade, K.A.
    Feature extraction is a proficient method for reducing dimensions in the analysis and prediction of cancer classification. Microarray procedure has shown great importance in fetching informative genes that needs enhancement in diagnosis. Microarray data is a challenging task due to high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, a comparative study to demonstrate the effectiveness of feature extraction as a dimensionality reduction process is proposed, and concludes by investigating the most efficient approach that can be used to enhance classification of microarray. Principal Component Analysis (PCA) as an unsupervised technique and Partial Least Square (PLS) as a supervised technique are considered, Support Vector Machine (SVM) classifier were applied on the dataset. The overall result shows that PLS algorithm provides an improved performance of about 95.2% accuracy compared to PCA algorithms. Keywords: Dimensionality Reduction, Principal Component Analysis, Partial Least Square, Support Vector Machine
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    A Comparative Analysis of Feature Selection and Feature Extraction Models for Classifying Microarray Dataset
    (School of Engineering and Computing, University of the West of Scotland - Computing and Information Systems Journal, 22(2), 29 – 38, 2018) Arowolo, M.O; Abdulsalam, S.O.; Isiaka, R.M.; Gbolagade, K.A.
    Purpose: The purpose of this research is to apply dimensionality reduction methods to fetch out the smallest set of genes that contributes to the efficient performance of classification algorithms in microarray data. Design/Methodology/Approach: Using colon cancer microarray dataset, One-Way- Analysis of Variance is used as a feature selection dimensionality reduction technique, due to its robustness and efficiency to select relevant information in a high-dimension of colon cancer microarray dataset. Principal Component Analysis (PCA) and Partial Least Square (PLS) are used as feature extraction techniques, by projecting the reduced high-dimensional data into efficient lowdimensional space. The classification capability of colon cancer datasets is carried out using a good classifier such as Support Vector Machine (SVM). The study is analyzed using MATLAB 2015. Findings: The study obtained high accuracies and the performances of the dimension reduction techniques used are compared. The PLS-Based attained 95% accuracy having edge over the other dimension reduction methods (One-Way- ANOVA and PCA). Practical Implications: The major implication of this research is getting the local dataset in the environments which lead to the usage of an open resource dataset. Originality: This study gives an insight and implications of high dimensional data in microarray gene analysis. The application of dimensionality reduction helps in fetching out irrelevant information that halts the performance of a microarray data technology. Keywords: Dimension Reduction, One-Way- ANOVA, PCA, PLS, Classification