Hybrid SVM and Resonance Neural Network Model for Precision Personality Profiling

Personality Profiling in contemporary applications of psychology, human resources and marketing. Conventional mean, although powerful in isolating the first order personality traits can be extremely noisy so that inferencing more complex trait as a second or third- orders could easily lead to citation bias. Here, a new Hybrid System of SVM and Resonance Neural Network RNN based intelligent technique for secured personality is presented through this work. By using SVMs to perform feature separation efficiently and RNN for extracting temporal knowledge, we could identify the subtitle personality characteristics which can be missed by standalone models. The results showed that the model was able to classify with accuracy a sample of 92.1%, compared to around 50%-70%; and out-performed state-of-art SVM or RNN models on an extensive dataset, such as ImageNet for image classification purpose Furthermore, precision, recall and F1-score metrics were increased by at least 4.5%, demonstrating the robustness of the hybrid model in real-world practice. This suggests an application of hybrid machine learning architectures might further the state- of-the-art in personality profiling.