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<p>The growing adoption of location-based services and mobile technologies has resulted in the extensive accumulation of address-tagged data across both commercial and public platforms. Leading location-based services are predominantly developed by commercial companies. Their online geocoding and address-matching solutions, however, do not permit users to modify their reference databases, which raises concerns regarding the accuracy of the geocoding process. In this study, we propose a feature selection framework aimed at enhancing online geocoding quality and overcoming the limitations of address matching. The proposed method integrates text similarity algorithms to improve address-matching result, achieving a significant accuracy gain of approximately 10–25% compared to standard outputs from services like Google Maps and ArcGIS Online. Unlike traditional approaches, this study specifically employs a feature selection framework to ‘reverse-engineer’ and rectify the opaque decision-making processes of commercial geocoders. Among the fourteen evaluated feature selection methods, mutual information-based selection and minimum redundancy-maximum relevance were identified as the most effective. The findings indicate that character-based text similarity algorithms are recommended for prioritization to further enhance the accuracy of online geocoding outputs.</p>

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Explainable address matching in online geocoding: filter-based feature selection and ensemble classification

  • 期刊论文

  • 发表日期：

    2026年1月2日

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  • Batuhan Kilic,
  • Onur Can Bayrak,
  • Fatih Gülgen,
  • Melis Uzar

摘要

The growing adoption of location-based services and mobile technologies has resulted in the extensive accumulation of address-tagged data across both commercial and public platforms. Leading location-based services are predominantly developed by commercial companies. Their online geocoding and address-matching solutions, however, do not permit users to modify their reference databases, which raises concerns regarding the accuracy of the geocoding process. In this study, we propose a feature selection framework aimed at enhancing online geocoding quality and overcoming the limitations of address matching. The proposed method integrates text similarity algorithms to improve address-matching result, achieving a significant accuracy gain of approximately 10–25% compared to standard outputs from services like Google Maps and ArcGIS Online. Unlike traditional approaches, this study specifically employs a feature selection framework to ‘reverse-engineer’ and rectify the opaque decision-making processes of commercial geocoders. Among the fourteen evaluated feature selection methods, mutual information-based selection and minimum redundancy-maximum relevance were identified as the most effective. The findings indicate that character-based text similarity algorithms are recommended for prioritization to further enhance the accuracy of online geocoding outputs.