# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 4 Experimental Evaluation
### Experimental Case 3: Address Matching Using Machine Learning  
For the address matching using machine learning, the Alias DB and training data derived from the Alias DB are needed. The Alias DB has one official name and its four aliases. We established four aliases systematically based on the rules of making alias attributes [18], as shown in Table 4. For instance, in Alias 1, a space is inserted after the first two characters. For Alias 2, a similar character replaces the first or second character. The Alias DB has 352 records for the set of official road names and their four aliases. Training data consisted of matching pairs and non-matching pairs, as described in Section 3.2 and had 1750 records. For matching pairs, each official name is paired with itself and each alias to calculate similarity values using seventeen similarity metrics. Additionally, we paired an official name and its four aliases with another similar official name, to make non-matching pairs. The reason why we chose similar names for non-matching pairs is to increase the performance of machine learning models when distinguishing a name against its similar form. One example of the non-matching \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Attribute**} \\ \hline Alias1 & Case using one space in official name \\ Alias2 & Case where the official name has only one character removed \\ Alias3 & Case where the official name has two characters removed \\ Alias4 & Case where the official name has only one misspelling \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Four attributes of address aliases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Matched Input Address**} & **Mismatched Input Address** & **Unmatched Input Address** \\ \hline Jaro-Winkler Sorted & & 81.23\% & 8.66\% & 10.11\% \\ Mlipns & Input data 1* & 40.59\% & 39.50\% & 19.95\% \\ [PERSON] & & 80.84\% & 8.53\% & 10.63\% \\ [PERSON] & & 42.71\% & 45.41\% & 11.88\% \\ \hline [PERSON] Sorted & & 85.30\% & 6.76\% & 7.94\% \\ Mlipns & Input data 2* & 57.48\% & 27.17\% & 15.35\% \\ [PERSON] & & 84.91\% & 6.63\% & 8.46\% \\ [PERSON] & & 58.86\% & 33.60\% & 7.55\% \\ \hline [PERSON] Sorted & & 89.76\% & 4.27\% & 5.97\% \\ Mlipns & Input data 3* & 74.74\% & 15.09\% & 10.17\% \\ [PERSON] & & 89.37\% & 4.13\% & 6.50\% \\ [PERSON] & & 75.79\% & 21.26\% & 2.95\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Geocoded on**} & **Geocoded Outside** & **Mean Distance \(\pm\)** \\ \multicolumn{2}{c}{**Corresponding Parcels**} & **Corresponding Parcels** & **Standard Deviation** \\ \hline Jaro-Winkler Sorted & & 34.51\% & 65.49\% & 4.40 \(\pm\) 7.06 m \\ Mlipns & Input data 1* & 17.65\% & 82.35\% & 4.50 \(\pm\) 6.95 m \\ Tversky & & 34.32\% & 65.68\% & 4.39 \(\pm\) 7.02 m \\ Monge-Elkan & & 18.11\% & 81.89\% & 5.12 \(\pm\) 8.03 m \\ \hline Jaro-Winkler Sorted & & 37.01\% & 62.99\% & 4.42 \(\pm\) 7.25 m \\ Mlipns & Input data 2* & 26.71\% & 73.29\% & 4.49 \(\pm\) 7.63 m \\ [PERSON] & & 36.81\% & 63.19\% & 4.41 \(\pm\) 7.20 m \\ Monge-Elkan & & 26.71\% & 73.29\% & 4.84 \(\pm\) 8.07 m \\ \hline Jaro-Winkler Sorted & & 38.71\% & 61.29\% & 4.45 \(\pm\) 7.26 m \\ Mlipns & Input data 3* & 33.46\% & 66.54\% & 4.47 \(\pm\) 7.40 m \\ [PERSON] & & 38.52\% & 61.48\% & 4.44 \(\pm\) 7.22 m \\ Monge-Elkan & & 33.46\% & 66.54\% & 4.62 \(\pm\) 7.57 m \\ \hline \hline \end{tabular}  
* Input data 1: 30% of correct matches; Input data 2: 50% of correct matches; Input data 3: 70% of correct matches.  
\end{table} 
Table 3: 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. pairs is <Hyowon 94 th street, Hyowon 9 th street>.