Chimpanzee Leader Election Optimization - Support Vector Machine Classifier-Based Analysis of Handwritten Digits Recognition

Hybrid models in machine learning (ML) for a case usually have good tendencies if they get the right conditions. This paper aims to classify the recognition of handwritten digits using hybrid modeling between the chimpanzee leader election optimization (CLEO) algorithm and support vector machine (SVM) classifiers. Handwritten digit recognition is a form of pattern recognition. Pattern recognition in images refers to the segmentation of detected image objects. The various patterns of handwritten digits will be related to the results and selection of patterns used to identify and equalize the model's perception of a particular pattern. The handwritten digits in this paper consist of the numbers 0 to 9. The amount of data is 1384 data. These data have then been divided into training and testing data of 984 and 400, respectively. Because the handwritten digit dataset is unbalanced, the data needs to be balanced using Synthetic Minority Over-Sampling Technique (SMOTE). The CLEO algorithm finds the best parameter values for SVM to operate in classification. It indicates the central role of the CLEO algorithm in optimizing the SVM model so that it tends to have the best evaluation results. Assigning an appropriate model in solving a case is an absolute necessity so that it can identify the data pattern and predictions on new data can be made. The hybrid model between the CLEO algorithm and the SVM classifier in this paper has obtained accuracy results for recognizing handwritten digits of 93%. This model was then used to compare between digits. However, the results were entirely satisfactory.