This paper proposes to design a majority vote ensemble classifier for accurate detection of credit card frauds. There is a lack of research studies on analyzing real-world credit card data owing to the issues of confidentiality, some features were hidden. Credit Card Fraud Dataset from kaggle.com was used for this study. The dataset is made up of 284,807 the number of legitimate transactions was found to be 284,315 while the number of fraudulent transactions was found to be 492, this shows that dataset is highly imbalanced, skewed data set like this may lead to an unreliable prediction performance, which is a major classification problem. Hence this study review literatures on methods of addressing the problem imbalance in dataset, and profile a methods of using the Voter Ensemble approach to address the classification problem. The real-world credit card dataset is analyzed to check for missing values and results show that there were no missing or duplicate values in the dataset. In this study, machine learning algorithms are used to detect credit card fraud. The base learners (Logistic regression, Bagging and Naïve Bayes) and the voter ensemble methods are combined. In this experiment, data are splits into 70% training and 30 % testing, the base (learners) classifiers were trained from the training sets, and predictions evaluations were made on data using 10-fold cross-validation. Afterward, the predictions of the learned base classifiers are combined into an ensemble using a stacked generalization strategy. The experimental results positively indicate that the Voter Ensemble method achieves good performances based on Precision, Recall, F1-score and Accuracy rates in detecting fraud cases in credit cards with 95.77%, 99.99%, 97.83%, 99.99% respectively. The result is evaluated and compared with other existing works. Keywords: Voter Ensemble, Logistic Regression (LR), Naïve Bayse, Bagging, imbalance ratio (IR), CCFD.
In the world of data mining, the k-means clustering algorithm is regarded as one of the most effective and well-liked methods. Although the approach is widely used, it does have certain drawbacks, such as issues with centroids' random initialization, which might result in unforeseen convergence. Moreover, the number of clusters that must be determined in advance for this type of clustering method is what determines the distinct cluster forms and outlier effects. The inability of the k-means algorithm to accommodate different data formats is a basic issue. This work used Halstead Complexity measure to find the software complexity of k- means algorithm. K-Means algorithm was written in C++, C#, and Java programming language. The software complexity of C++, C#, and Java programming language was evaluated using Halstead Complexity measure. The result obtained was compared in order to discover the complexity of all the different implementation languages. Three different codes of K-means algorithm were written in C++, C# and Java programming language. Halstead complexity measure was used to evaluate the different implementation structures of programming languages for comparative analysis of complexity measure. Comparatively, the results showed that Java programming language performed better than C++ and C# in vocabulary of program, estimated program level, effort to generate program and programming time. In this work, it was discovered that Java has the smallest elementary mental discrimination time to construct a program which is 15.426 seconds when compare to the others. Key information about software testability, dependability, and maintainability may be predicted using complexity measurements from computerized source code assessment. Keywords: Clustering Algorithm, Complexity, Convergence, Source Code, Software, Vocabulary Lala, O.G., Onamade, A.A., Oduwole, O.A., Sunday, P., Aroyehun, A.A. & Olabiyisi, S.O. (2022): Performance Evaluation Of The Effect Of Implementation Languages On The Sofware Complexity Of K- Means Algorithm. Journal of Advances in Mathematical & Computational Sciences. Vol. 9, No. 2. Pp 61-74. DOI: dx.doi.org/10.22624/AIMS/MATHS/V10N2P6 Available online at www.isteams.net/mathematics-computationaljournal.
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