# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Result
#### 5.5.2 Comparison Results
* H7\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and LSTM. * H8\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN-LSTM. * H9\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Naive Bayes. * H10\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Random Forest. 3. Platform Dataset: * H11\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN. * H12\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and LSTM. * H13\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN-LSTM. * H14\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Naive Bayes. * H15\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Random Forest. 4. KDE Dataset: * H16\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN. * H17\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and LSTM. * H18\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN-LSTM. * H19\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Naive Bayes. * H20\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Random Forest. 5. Core Dataset: * H21\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN. * H22\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and LSTM. * H23\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and CNN-LSTM. * H24\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Naive Bayes. * H25\({}_{0}\): No significant difference exists in duplicate detection performance between the proposed model and Random Forest. (7)  
Thunderbird Dataset:  
* H31\({}_{\text{a}}\), H32\({}_{\text{a}}\), H33\({}_{\text{a}}\), H34\({}_{\text{a}}\), H35\({}_{\text{a}}\): A significant difference exists in duplicate detection performance between the proposed model and the baseline models (CNN, LSTM, CNN-LSTM, Naive Bayes, Random Forest). The statistical results, summarized in Table 3, confirmed that the proposed model showed a statistically significant improvement over all baseline models. For instance, the p-values for comparisons with CNN, LSTM, CNN-LSTM, Naive Bayes, and Random Forest in Apache were all below 0.05, rejecting the null hypotheses and supporting the alternative hypotheses. This pattern was consistent across all other projects, including JDT, Platform, KDE, Core, Firefox, and Thunderbird.