A hybrid FAM–CART model and its application to medical data classification

A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models

To predict the class level of any classification problem, predictive models are used and mostly a single predictive model is built to predict the class level of any classification problem; current research considers multiple predictive models to predict the class level. Ensemble modeling means instead of building a single predictive model, it is proposed to build a multilevel predictive model, which generalizes to predict all the class levels with an adequate percent of accuracy, that is, from 70% to 90% by applying and using a different combination of classification algorithms. In this article, a multilevel approach for selecting base classifiers for building an ensemble classification model is proposed. The rudimentary concept behind this approach is to drop lousy performing features and collinearity from the selected data set for ensemble modeling. For the evaluation of the proposed multilevel predictive model, different data sets from the University of California, Irvine, repository have been used and comparisons with the modern classifier's models have been conducted. The implementation analyses demonstrate the potency and excellence of the novel approach when compared with other modern classification models (three-layered artificial neural network, Radial Variant Function Neural Network/Fish Swarm Algorithm). The classification accuracy achieved with selected algorithms lies in the range of 70%-88.3%. Among all the selected classification algorithms, the lowest accuracy is achieved by the naive Bayes algorithm, which is close to 71.9%. However, the proposed algorithm (NB-RF-LR-SEMod), which is a combination of different classifiers, achieved a maximum accuracy of 88.3% on the Photographic and Imaging Manufacturers Association Diabetes data set, which is, by far, the best to any single classifier. Hence, this proposed work is helpful for any health care official to detect the diabetes problem at an early stage and prevent the affected person from future complications of it.

An intelligent payment card fraud detection system

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.