# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 3 The Roadmap of Existing Literature
### Background Knowledge
#### 3.2.2 Similarity Evaluation Model
Each decision tree in RF is trained independently by randomly selecting N samples with replacements. During the training process, RF randomly selects m sample features and, according to a preset strategy, chooses one of them as the splitting attribute for a node. When making predictions for a given instance, the majority classification result from the decision trees is selected. Extreme tree, on the other hand, is an even more aggressive strategy within random forest. It uses the entire training set during the bootstrap process and randomly selects splitting attributes when constructing decision trees. (7) Hidden Markov Model (HMM)  
The hidden Markov model (HMM) can make predictions about possible states based on current observations and adjust the model's parameters using the observed and predicted results. An HMM can be described using a five-tuple,  
\[HMM=(X,O,\pi,A,B) \tag{20}\]  
where \(X\) represents the hidden state vector, \(O\) represents the observed state vector, \(A\) is the hidden state transition matrix, \(B\) is the confusion matrix representing the probabilities of observed states given hidden states, and \(\pi\) is the initial probability vector for hidden states. In the context of text classification, the training texts are first subjected to feature extraction to obtain features such as frequency and probability distributions. These features are used to form the initial state vector. By training the HMM with the corresponding class labels of the texts, the parameters of the HMM are determined. This trained HMM can then provide a probability distribution for classification of test texts. (8) LSTM/CNN  
Convolutional Neural networks (CNNs) are widely used in image processing and have achieved outstanding results. Recently, there have been efforts to apply CNNs to text classification tasks. The approach and model construction are similar to those used in image processing. As shown in Figure 8, the text is first converted into vectors using techniques like word2 vec. Then, convolutional operations, pooling, and other operations are performed to generate probability outputs corresponding to the classification results. LSTM (long short-term memory) is a variant of a recurrent neural network (RNN) that excels at capturing spatial dependencies in text, making it widely used in text-related fields such as natural language processing (NLP). As shown in Figure 9, LSTM is an extended version of RNN. It introduces a forget gate, which determines what information should be retained and what should be forgotten. This helps alleviate the vanishing gradient problem caused by forgetting hidden state variables in RNNs. Consequently, LSTM can achieve higher accuracy in text classification tasks. In some works, CNN and LSTM are also combined to leverage the strengths of both architectures. Performing transfer learning based on existing models is an effective method for text classification, as it reduces the time and complexity of model construction. As shown in Figure 10, transfer learning involves fine tuning of the parameters of a pretrained model to adapt to a new optimization objective. Figure 8: Example image of a CNN for text classification.