# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 5 Conclusions  
This study suggested an algorithm for accurate and stable geocoding. Address parsing helped to obtain meaningful units of addresses to send them to the matching process. We introduced machine learning techniques in order to achieve high accuracy in the address matching process. It was proved that XGB with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of XGB outperformed similarity metrics when using training data or input data. The performance of XGB was also consistent across different kinds of input data. The address matching process using machine learning was able to deal with human errors, including spelling errors, in input addresses to match addresses accurately. As a module in the suggested geocoding system, it can be applied to any other geocoding system to precisely convert addresses into geographic coordinates for relevant research and applications. The address locating allowed to narrow down to a road segment from the candidate road segments selected in the address matching and convert addresses into one geocoded point on a map.  
Figure 11: Accuracy of address matching in three experimental cases.  
This study, however, has some limitations. First, the performance of the matching process was dependent on the quality of the Alias DB. Training data only had 1750 records for matching and non-matching pairs, which is an insufficient number to make a robust machine learning method. For each official name, we only made four different kinds of aliases. There may be some excluded aliases in the Alias DB and training process. Thus, for future work, more matching cases with more aliases need to be considered in machine learning methods to enhance the robustness of the trained methods. Second, the proposed algorithm was only applicable for street-based addresses. Apart from the street-based addresses, there are area-based addressing systems and hybrid addressing systems [5], and the proposed algorithm did not consider those two addressing systems. Hierarchical area-based addressing systems are used in Eastern Asia, while China has a hybrid addressing system. In order to apply to various countries, we suggest the expansion of the proposed algorithm to embody the two different addressing systems for future research.  
Moreover, we need to put some efforts on decreasing mismatched addresses. Mismatched addresses ended up being geocoded in other locations far away from correct places and decreased the positional accuracy of geocoding. Therefore, by finding a way to complement the machine learning (e.g., adding additional rules), we need to avoid making mismatching. Lastly, this study did not consider unstructured addresses in the parsing step of the geocoding. This study assumed that the input addresses are structured and normalized, and no error occurred in the parsing process. Although machine learning techniques in the matching process can handle abbreviations, misspellings, and misplacements, to achieve high performance of the geocoding, the normalization, like removing punctuation and standardization is necessary to some extent [49]. Therefore, future work needs to strengthen the parsing process.  
Conceptualization, J.L. and K.L.; methodology, K.L. and A.R.C.C.; software, K.L. and A.R.C.C.; validation, K.L., A.R.C.C. and J.L.; formal analysis, K.L. and A.R.C.C.; investigation, K.L.; resources, K.L.; data curation, K.L.; writing--original draft preparation, K.L. and A.R.C.C.; writing--review and editing, A.R.C.C. and J.L.; visualization, K.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1D1A1B03028890). The authors declare no conflict of interest.