Browsing by Author "Salihu, Shakirat Aderonke"

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    Comparative Analysis of Selected Supervised Classification Algorithms.
    (Computer Chapter of the Institute of Electrical & Electronics Engineers (IEEE) Nigeria Section., 2015-10) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Salihu, Shakirat Aderonke; Oladipupo, Kehinde Razak
    Information is not packaged in a standard easy-to-retrieve format. It is an underlying and usually subtle and misleading concept buried in massive amounts of raw data. From the beginning of time it has been man’s common goal to make his life easier. The prevailing notion in society is that wealth brings comfort and luxury, so it is not surprising that there has been so much work done on ways to sort large volume of data. Over the year, there are various data mining techniques and used to sort large volume of data. This paper considers Classification which is a supervised learning technique. Therefore the need to come up with the most efficient way to deal with voluminous data with very little time frame has been one of the biggest challenges to the AI community. Hence, this paper presents a comparative analysis of three classification algorithms namely; Decision Tree (J-48), Random Forest and Naïve Bayes. A 10-fold cross validation technique is used for the performance evaluation of the classifiers on KDD’’99, VOTE and CREDIT datasets using WEKA (Waikato Environment forKnowledge Analysis) tool. The experiment shows that the type of dataset determines which classifier is suitable.
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    Enhanced Classification via Clustering Techniques using Decision Tree for Feature Selection
    (Foundation of Computer Science (FCS), NY, USA, 2015-09-01) Balogun, Abdullateef Oluwagbemiga; Mabayoje, Modinat Abolore; Salihu, Shakirat Aderonke; Salvation, Arinze
    Information overload has raggedly increased as a result of the advances in the aspect of storage capabilities and data collection in previous years. The growth seen in the number of observation has partly cause a collapse in analytical method but the increases in the number of variable associated with each observation has grossly collapse it. The number of variables that are measured on each observation.is referred to as the dimension of the data, and a major problem of dataset containing high dimensions is that, there exist only few “important” measured variables for understanding the fundamental occurrences of interest. Hence, dimension reduction of the original data prior to any modeling of the data is of great necessity today. In this paper, a précis of K-Means, Expectation Maximization and J48 decision tree classifier is presented with a framework on the performance measurement of base classifiers with and without feature reduction. A performance evaluation was carried out based on F-Measure, Precision, Recall, True Positive Rate, False Positive Rate, ROC Area and Time taken to build model. The experiment revealed that the reduced dataset yielded improved results than the full dataset after performing classification via clustering.