RESUMO
Nowadays, deep learning methods with transfer learning (TL) makes ease of stress emotion classification tasks. Amongst, an optimized convolutional neural network with TL (OCNNTL) executes OCNN-based classification on emotion and stress data domains to learn high-level features at the top layers. However, it fails to handle the abrupt concept drift in real-time; besides, it end up with huge time complication while on gathering the required data and its transformation. To tackle the aforementioned concerns, a novel online OCNNTL (O2CNNTL) model is proposed; whereas, OCNNTL process initiates in the stress-emotion domain via the prior knowledge acquired by learning the training data both from the stress as well as the emotion domains. Moreover in O2CNNTL model, the concept-drifting data streams are taken into account for solving the online classification by the OCNN classifier; whereas, to enhance the learning efficiency a regularization learning technique is instigated on varied feature spaces. Thus, the proposed O2CNNTL achieves higher efficiency than the state-of-the-art models.
