Performance tradeoff between evolutionary computation (EC)/adaptive boosting (AB) hybrid and support vector machine breast cancer classification paradigms

This paper describes a breast cancer classification performance trade-off analysis using two computational intelligence paradigms. The first, an evolutionary programming (EP)/adaptive boosting (AB) based hybrid, intelligently combines the outputs from an iteratively "called" weak learning algorithm (one which performs at least slightly better than random guessing) in order to "boost" the performance of an EP-derived weak learner. The second paradigm is support vector machines (SVMs). SVMs are new and radically different types of classifiers and learning machines that use a hypothesis space of linear functions in a high dimensional feature space. The most important advantage of a SVM, unlike neural networks, is that SVM training always finds a global minimum. Furthermore, the SVM has inherent ability to solve pattern classification without incorporating any problem-domain knowledge. In this study, the both the EP/AB hybrid and SVM were employed as pattern classifiers, operating on mammography data used for breast cancer detection. The main focus of the study was to construct and seek the best EP/AB hybrid and SVM configurations for optimum specificity and positive predictive value at very high sensitivities. Using a mammogram database of 500 biopsy-proven samples, the best performing SVM, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 45.0% and a positive predictive value (PPV) of 50.1% at 100% sensitivity. At 97% sensitivity, a specificity of 55.8% and a PPV of 55.2% were obtained. The best performing EP/AB hybrid obtained slightly lower, but comparable, results.