Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification

Abstract

Focusing on emotion recognition, this paper addresses the task of emotion classification and its performance with respect to accuracy, by investigating the capabilities of a distributed ensemble model using precision-based weighted blending. Research on emotion recognition and classification refers to the detection of an individual’s emotional state by considering various types of data as input features, such as textual data, facial expressions, vocal, gesture and physiological signal recognition, electrocardiogram (ECG) and electrodermography (EDG)/galvanic skin response (GSR). The extraction of effective emotional features from different types of input data, as well as the analysis of large volume of real-time data, have become increasingly important tasks in order to perform accurate classification. Taking into consideration the volume and variety of the examined problem, a machine learning model that works in a distributed manner is essential. In this direction, we propose a precision-based weighted blending distributed ensemble model for emotion classification. The suggested ensemble model can work well in a distributed manner using the concepts of Spark’s resilient distributed datasets, which provide quick in-memory processing capabilities and also perform iterative computations effectively. Regarding model validation set, weights are assigned to different classifiers in the ensemble model, based on their precision value. Each weight determines the importance of the respective classifier in terms of its performing prediction, while a new model is built upon the derived weights. The produced model performs the task of final prediction on the test dataset. The results disclose that the proposed ensemble model is sufficiently accurate in differentiating between primary emotions (such as sadness, fear, and anger) and secondary emotions. The suggested ensemble model achieved accuracy of 76.2%, 99.4%, and 99.6% on the FER-2013, CK+, and FERG-DB datasets, respectively.

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

“… Feature extraction, which is pre-trained network which can be used as a feature extractor using the activation layers as features, and these layers can be used to train other machine learning models, such as a support vector machine (SVM) [ 62 , 77 , 83 , 90 , 101 ]. Transfer learning, in which the layers of a neural network trained on one dataset are adjusted and reused to test a new dataset [ 54 , 73 , 102 , 103 , 104 ]. …”

“…Transfer learning, in which the layers of a neural network trained on one dataset are adjusted and reused to test a new dataset [ 54 , 73 , 102 , 103 , 104 ].…”

Special Issue on Ensemble Learning and/or Explainability

“…The fourth paper is entitled "Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification" and it is authored by Soman et al [6]. In this study, the authors conduct emotion classification by exploring the capabilities of a distributed ensemble-based model utilizing precision-based weighted blending.…”

Emotional State Detection Using Electroencephalogram Signals: A Genetic Algorithm Approach

“…The development of user-centric artificial intelligence-based technologies has been one of the main reasons for the growth in different application areas such as healthcare, education, entertainment, robotics, marketing, security, and surveillance. Physical expressions such as facial gestures, speech or postures have been used to identify human emotions [1][2][3][4][5][6], but in some cases, this can be ineffective because people may purposely or unconsciously mask their true feelings, which is why physiological signals can provide a more precise and objective recognition of emotions [7]. For this reason, many of the approaches in affective computing research have turned their attention to analysis through physiological signals [8][9][10][11][12][13][14][15].…”