Employing an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records
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When collecting person records for census, identifying individuals accurately is paramount. Over time, people change their phone numbers, their addresses, even their names. Without a universal identifier such as a social security number or a finger-print, it is difficult to know whether two distinct person records represent the same individual. The Cost Sensitive Alternating Decision Tree (CSADT) algorithm (a supervised learning algorithm) is employed as a Record Linkage solution to the problem of resolving whether two person records are the same individual. A person record consists of several attributes such as a name, a phone number, an address, etc. The number of person-record-pairs grows exponentially as the number of records increase. In order to accommodate this exponential growth, a scalable implementation of the CSADT algorithm was employed. A thorough investigation and evaluation are presented demonstrating the effectiveness of this implementation of the CSADT algorithm on linking person records.