# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 4 Experimental Evaluation
### Comparison of the Results of Three Experimental Cases  
Table 7 and Figure 11 show the results of all the three experimental cases. The accuracy of the address matching with or without similarity metrics and machine learning increases along with the ratio of correct matches, which increases from Input data 1 to Input data 3. Results show that address matching using machine learning outperforms the matching with or without similarity metrics. Notably, the address matching using XGB is 7% higher than Jaro-Winkler Sorted and Tversky and 21% higher than Mlipns and Monge-Elkan when using input data with 70% of correct matches. When input data have 30% of correct matches (Input data 1), the difference of accuracy between XGB and Jaro-Winkler Sorted and Tversky becomes 14%, which is two times more than input data with 70% of correct matches. Further, the difference of accuracy between XGB and Mlipns and Monge-Elkan becomes 53-55%, which is 2.5 times more than input data having 70% of correct matches. It indicates that the performance of Mlipns and Monge-Elkan drops more than Jaro-Winkler Sorted and Tversky as the percentage of correct matches in the input data decreases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Matched Input Address** & **Mismatched Input Address** & **Unmatched Input Address** \\ \hline Input data 1 * & 95.74\% & 1.90\% & 2.36\% \\ Input data 2 * & 96.33\% & 1.31\% & 2.36\% \\ Input data 3 * & 96.39\% & 1.25\% & 2.36\% \\ \hline  & **Geocoded on** & **Geocoded Outside** & **Mean Distance \(\pm\)** \\  & **Corresponding Parcels** & **Corresponding Parcels** & **Standard Deviation** \\ \hline Input data 1 * & 40.62\% & 59.38\% & 4.44 \(\pm\) 7.09 m \\ Input data 2 * & 40.94\% & 59.06\% & 4.48 \(\pm\) 7.21 m \\ Input data 3 * & 40.94\% & 59.06\% & 4.47 \(\pm\) 7.21 m \\ \hline \multicolumn{4}{l}{* Input data 1: 30\% of correct matches; Input data 2: 50\% of correct matches; Input data 3: 70\% of correct matches.} \\ \end{tabular}
\end{table} 
Table 6: Performance of address matching process with a similarity metric, percentage of geocoded input addresses on corresponding parcels, and distances between geocoded addresses and the corresponding parcels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experimental Case** & **Input Data 1 * & **Input Data 2 * & **Input Data 3 * \\ \hline Experimental case 1 & 34.12\% & 52.82\% & 71.78\% \\ \hline  & [PERSON] & & & \\  & Sorted & & & \\ Experimental case 2 & Mlipns & 40.55\% & 57.48\% & 74.74\% \\  & [PERSON] & 80.84\% & 84.91\% & 89.37\% \\  & Monge-Elkan & 42.71\% & 58.86\% & 75.79\% \\ \hline Experimental case 3 & XGB & 95.74\% & 96.33\% & 96.39\% \\ \hline \hline \multicolumn{4}{l}{* Input data 1: 30\% of correct matches; Input data 2: 50\% of correct matches; Input data 3: 70\% of correct matches.} \\ \end{tabular}
\end{table} 
Table 7: Accuracy of address matching in three experimental cases. The performance of XGB is consistently high when compared to address matching with or without similarity metrics. The accuracy of XGB is 95-96% across input data with different percentages of correct matches, whereas the accuracy of address matching with or without similarity metrics is relatively low and varies more. Especially, Jaro-Winkler Sorted and Tversky vary from around 80% in input data with 30% of correct matches, to 89% in input data with 70% of correct matches. When XGB gains 1%, Jaro-Winkler Sorted and Tversky change 9%. As a result, the address matching using machine learning is more stable and accurate to use for geocoding than simple matching or matching with a similarity metric. Combining the capabilities of various similarity metrics using machine learning is better than using each similarity metric for geocoding. Further, among the four kinds of similarity metrics, Jaro-Winkler Sorted and Tversky are more consistent across input data with different ratios of correct matches than Mlipns and Monge-Elkan. When Jaro-Winkler Sorted and Tversky vary 9%, Mlipns and Monge-Elkan change approximately 34%.