Evolving Fuzzy Rules for Breast Cancer Diagnosis
Carlos Andrés Peña-Reyes, Moshe Sipper · 1998
We present an evolutionary approach for discovering fuzzy systems for breast cancer diagnosis. By judiciously designing an appropriate representation scheme (genome) and fitness function, the genetic algorithm is then able to produce successful systems. These surpass the best known systems to date in terms of combined performance and simplicity. I. Introduction Fuzzy logic is a computational paradigm that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning [1]. A prime characteristic of fuzzy logic is its capability of expressing knowledge in a linguistic way, allowing a system to be described by simple, "human-friendly" rules. A fuzzy inference system is a rule-based system that uses fuzzy logic, rather than boolean logic, to reason about data [1]. Its basic structure comprises four main components: (1) a fuzzifier, which translates crisp (real-valued) inputs into fuzzy values, (2) an inference engine that applies a fuzzy reaso...