Paper Info
Title
Geographical address classification without using geolocation coordinates
Abstract
Online retail focuses on optimal delivery system of ordered shipments. In the Last Mile context of a Supply Chain, automatic categorization of addresses is an important problem. An automated solution to this problem reduces manual effort significantly from physical reading of shipment addresses to automatic identification of the corresponding route. In general, addresses help to relate to a geolocation. In the absence of geolocation information in terms of latitude and longitude of individual houses and a definitive structure in the addresses, classifying a given address as belonging to a particular locality is a challenging task. In the current work we devised an accurate method to classify the addresses belonging to a region as belonging to predefined subregions in the background of the above challenges. The activity involves text processing, address preprocessing, clustering, classification using ensemble of classifiers, efficient ways to deal with large dataset with high dimensionality and increasing labeled dataset using semi-supervised classification. We discuss each of these stages that culminates in classification of addresses into sub-localities with a high classification accuracy. The solution is demonstrated in an operational setting in a major e-commerce organization. The solution is applicable to developing countries where geolocation information is not completely available.
Year
DOI
Venue
2015
10.1145/2837689.2837696
Geographic Information Retrieval
Field
DocType
Citations 
Data mining,Locality,Computer science,Geolocation,Data pre-processing,Artificial intelligence,Cluster analysis,Text processing,Domain knowledge,Information retrieval,Last mile,Preprocessor,Machine learning
Conference
2
PageRank 
References 
Authors
0.52
9
5
Name
Order
Citations
PageRank
T. Ravindra Babu1576.26
Abhranil Chatterjee292.09
Shivram Khandeparker320.52
A. Vamsi Subhash420.52
Sawan Gupta520.52