# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Baseline  
In this study, several machine learning and deep learning models were employed as baselines to evaluate the performance of the proposed model. The baseline models include CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest. Below is an overview of these models and their roles:  
1. Convolutional Neural Network (CNN) [40]: A CNN is a type of neural network that extracts features from input data using convolution operations. This approach groups and processes adjacent data, making it particularly effective for identifying spatial patterns in three-dimensional data such as images or sequences. The extracted features are passed through successive layers for further processing. CNNs are widely used in tasks requiring the preservation of spatial information and the identification of local features.
2. Long Short-Term Memory (LSTM) [41]: LSTM is a specialized type of recurrent neural network (RNN) designed to address the limitations of traditional RNNs, particularly their inability to capture long-term dependencies. LSTM achieves this by incorporating memory cells and gates, which allow the model to retain information from both recent and distant past inputs. This capability makes LSTM suitable for sequential data tasks, such as time series forecasting and natural language processing.  
3. CNN-LSTM [41]: CNN-LSTM is a hybrid model that combines the strengths of CNNs and LSTMs. In this architecture, CNN layers are used to extract spatial features from the input data, while LSTM layers process the temporal context of the extracted features. This combination is particularly effective for tasks that involve sequential data with underlying spatial patterns, such as video analysis or time-dependent text classification.
4. Naive Bayes [42]: Naive Bayes is a probabilistic classification algorithm based on Bayes' theorem. It assumes conditional independence among the features, which simplifies the computation of class probabilities. Despite its simplicity, Naive Bayes is effective for tasks where the independence assumption is approximately valid. It is commonly used in text classification and spam detection tasks.
5. Random Forest [42]: Random Forest is an ensemble learning algorithm that builds multiple decision trees during training and aggregates their predictions to improve accuracy and robustness. It addresses the limitations of single decision-tree models, such as overfitting and lack of generalization, by combining the results of multiple trees. This approach enhances stability and predictive power, making it suitable for both classification and regression tasks. Significance of the Baseline Models. The inclusion of these baseline models provides a comprehensive comparison framework for evaluating the effectiveness of the proposed approach. Each baseline offers unique advantages and limitations: * CNN excels in feature extraction for spatial data. * LSTM handles sequential dependencies in temporal data. * CNN-LSTM captures both spatial and temporal patterns simultaneously. * Naive Bayes offers simplicity and computational efficiency for probabilistic classification tasks. * Random Forest enhances accuracy and stability through ensemble learning. By comparing the performance of the proposed model against these diverse baselines, the study ensures a robust and objective evaluation of its effectiveness in duplicate bug report detection.