# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 2 Literature Review and Design Theories
### Unknown Intent Detection Technologies and Decision Boundary Learning  
To address utterances with unknown intent, researchers have developed a paradigm that utilizes advanced deep learning technology to obtain intent decision boundaries and exclude queries with unknown intents in a two-step learning method [35]. The design paradigm is free from dependence on utterance samples with unknown intents, so the efforts are mainly paid to exploring available semantic information and outlining intent decision boundaries. First, a \(K\)-class deep learning classifier is trained to classify user utterances into \(K\) known intents in a supervised learning manner, learning deep discriminative features that capture the relationship between utterances and their intents. The deep learning network removing the last classification layer can be used to transform a query into a new representation. The decision space we expect to obtain is the high-dimensional feature space in which these transformed query representations exist. Then, an unknown intent detection algorithm is designed to identify whether a given utterance relates to unknown intents. It functions by outlining decision boundaries based on corresponding transformed representations of each known intent class. A given representation beyond all known intent boundaries is regarded as involving an unknown intent. The classifier's feature extraction performance has proven effective for decision space construction with the Transformer architecture and pre-training techniques. For example, a Transformer variant named BERT [36] has become state-of-the-art in textual feature learning. Its architecture can handle a whole sentence parallelly and globally. The multi-head self-attention mechanism effectively captures long-range dependencies in medical dialogues, and positional encoding preserves word sequence information critical for intent detection. Pre-training further enhances BERT's capabilities in contextual under standing and medical domain adaptation. These techniques make BERT-based large language models widely utilized for textual feature representation and decision space determination in recent unknown intent detection research [13; 35; 37; 38; 39; 40]. To train the classifier, optional loss functions for capturing deep discriminative features include cross-entropy loss [13; 37], large margin loss [11; 32], and contrastive learning loss [38; 40]. These loss designs mainly follow the computational principle of maximizing inter-class variance and minimizing intra-class variance [38]. In the decision space constructed from transformed representations, various detection methods have been proposed to determine boundary conditions and exclude utterances with unknown intents. Local Outlier Factor (LOF) is a classical density-based anomaly detection algorithm [41]. It was used to compute the local density deviation of a given utterance representation concerning its neighbors and determine the intent class boundaries in the decision space [11; 32; 38]. Maximum Softmax Probability is a detection method based on probability distribution [42]. It relied on a Softmax output to obtain the query sample's Maximum Softmax Probability and determine the boundary [38; 40]. Researchers have also developed distance-based designs to obtain decision boundaries. For example, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [37] adopted the Mahalanobis distance to set the threshold and form the boundary. [PERSON], [PERSON], [PERSON], and [PERSON] [13] proposed an Adaptive Decision Boundary (ADB) framework based on Euclidean distance to enhance the distinguishing capability of intent representations and learn tight decision boundaries adaptive to the feature space. This strategy has proven effective in detecting unknown intent and obtaining the best performance in public datasets. Existing research mainly emphasizes on extracting discriminative semantic features from user utterances and constructing transformed representations to define intent boundaries within the decision space.