Evaluating the Performance of an Incremental Classifier using Clustered-C4.5 Algorithm for Processing Big Data Streams
E. Afreen Banu, P Robert · 2024
Currently, the inevitability of big data stream processing is increasingly becoming more critical and imperative due to the incremental nature of the heterogeneous and massive volume of data engendered by various sources. C4.5 is the most renowned algorithm to address the problem of data stream classification in online mode by constructing decision trees. But, the traditional C4.5 algorithm has a significant adverse impact on the speed of data stream processing in real-time scenarios. To handle this problem, we propose a new variant of the C4.5 classifier, named the Clustered-C4.5 (CC4.5) algorithm to classify data streams in online mode. This work integrates a Correlation-based Feature selection and Bat Optimization Technique (CFBOT) with the CC4.5 algorithm to improve the classification performance. The CFBOT is employed to measure the dependencies among selected features and to determine the ideal subset for training and testing tasks. Meanwhile, CC4.5 organizes the data into smaller clusters instead of building a single decision tree. The integration of the CC4.5 algorithm and CFBOT helps the system classify imbalanced and multi-class datasets effectively. The performance of the proposed classifier is cautiously studied on the real-world dataset (i.e., UCI-heart disease dataset) by relating its enactment with many state-of-the-art approaches regarding classification accuracy, sensitivity, specificity, the area under the receiver operating characteristic curve (AUC), and intersection over-union (IoU). Additionally, the Wilcoxon rank-sum test is employed to assess whether our classification technique offers a statistically significant improvement over other classifier methods. The experimental results demonstrate that the proposed classifier outperforms other approaches with superior accuracy, sensitivity, specificity, AUC, and IoU of 93.6%, 96.2%, 79.3%, 92.3%, and 90.3%, correspondingly. Accordingly, we can conclude that CC4.5 is the better data stream classification model related to classification approaches recently reported in the literature.