# Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification
## 1 Introduction  
Emotions have a vital role in the development of human-computer interaction and humanoid robots. This is due to the fact that, by incorporating the idea of emotion understanding, intelligent software systems become more efficient and intuitive, as well as more akin to human-human communication [1]. Human beings convey their emotions in everyday encounters through vocal, hand, face, and body gestures. Because most human-to-computer interfaces (HCI) rely on simple interactions via traditional devices, such as keyboards, mice, touch screens, and so on, emotion identification and classification can help HCI devices function more efficiently. Textual data, facial expressions, verbal, gesture, and physiological signal recognition, electrocardiogram (ECG) and electrodermography (EDG)/galvanic skin response (GSR), and so on, can all be used to recognize and classify an individual's emotional state. Emotional state recognition can help increase access toidentification. A recent study has revealed that facial expressions is the most expressive method in which humans display emotions [2]. Facial expression detection has numerous uses, including non-intrusive sensor creation, medical teaching, telecommunications, police enforcement, lifelike software agents, and so on [2]. Many scholars have investigated so far various methods of emotion classification, with the most basic ones being happiness, sadness, anger, fear, disgust and surprise, all of which are based on a two-dimensional plane known as the valence-arousal plane. The discrete fundamental emotion description approach is used for this classification [3]. Primary and secondary emotions are two further types of emotions that are frequently employed [4]. Fear, joy, contempt, sadness, and surprise are examples of primary emotions, while feelings that create a mental image that links to memory or another primary emotion are examples of secondary emotions. Emotions are classified into two (valence and arousal) or three (valence, arousal, and dominance) dimensions using dimension-based techniques. Valence describes a person's level of positivity or negativity, whereas arousal describes the level of enthusiasm or indifference of emotion [5]. The dominance scale runs from submissive (no control) to dominance (empowered). Various methods are utilized to recognize and classify human face expressions, with ensemble machine learning techniques outperforming other methods in terms of performance. Ensemble methods are machine learning algorithms that combine many base models to improve each base model's stability and predictive capability [6]. They are attempting to improve forecast performance by merging predictions from multiple models. Rather than relying on a single model and hoping for the best, a decision maker can utilize an ensemble method, which involves taking a sample of models into account, calculating which features to employ, and determining the final prediction based on the sampled models' aggregated results. There is no limit to the amount of ensembles a decision maker can create for a particular predictive modelling task; nonetheless, two types of approaches dominate the field of ensemble learning: bagging [7] and boosting [8]. The majority of machine learning models work differently, and each model may perform well on some data while performing poorly on others; however, when all of them are combined, the ensemble model may cancel out each other's flaws. As a result, ensemble learning is frequently effective and can be used for both prediction and classification tasks. For example, several machine learning approaches can be coupled to recognize and classify human facial emotions. The performance of the chosen machine learning technique, however, degrades as the amount of the used datasets grows. To tackle this difficulty, we might adopt a distributed environment in which a deep machine learning algorithm can perform well.