Ensemble Learning Approach for Identifying Personality Traits based on Individuals' Behavior

Understanding human personality is essential for natural and social engagement. Arises a significant connection between users' personalities and their behavior. Our primary goal is to identify and classify individuals' personality traits based on their behaviors. Understanding personality types can help better understand preferences and potential differences between them. This study uses users' answers based on the questionnaire on personality to automatically identify the personality type based on behaviors. After pre-processing the data, we researched many classification techniques for automated recognition, including Naive Bayes, Support Vector Machine (SVM), Multilayer Perception (MLP), Random Forest, Logistic, and Decision Tree using a 10-fold cross-validation method. The second observational study combined all the algorithms using an Ensemble Learning algorithm, where by Vote algorithm. Accuracy, precision, recall, f-measure, and error values have been used to measure the systems' performance. According to the comparison analysis, SVM outperforms (85.8%) the other five personality trait detection algorithms. But after combing the five algorithms which contain the highest accuracy by Ensemble Learning algorithm obtained the highest performance (90.5%) than the SVM algorithm and obtained the highest recall, f-measure, and precision values, and the lowest error rates. It demonstrates that an ensemble learning approach that incorporates multiple distinct algorithms may yield greater accuracy than any one of its individual algorithms separately. Our findings are helpful for understanding how to manage and make a relationship with humans by predicting their personality earlier.