On Ternary Coding and Three-Valued Logic
This work addresses representation efficiency for AI and medical applications, but it is incremental as it builds on existing comparisons without introducing new methods.
The paper compares binary and ternary coding efficiencies in number representation and decision trees, finding ternary coding preferable for classification in AI and medicine, though implementation challenges persist.
Mathematically, ternary coding is more efficient than binary coding. It is little used in computation because technology for binary processing is already established and the implementation of ternary coding is more complicated, but remains relevant in algorithms that use decision trees and in communications. In this paper we present a new comparison of binary and ternary coding and their relative efficiencies are computed both for number representation and decision trees. The implications of our inability to use optimal representation through mathematics or logic are examined. Apart from considerations of representation efficiency, ternary coding appears preferable to binary coding in classification of many real-world problems of artificial intelligence (AI) and medicine. We examine the problem of identifying appropriate three classes for domain-specific applications.