OPTIMIZED DIAGNOSTIC MODEL COMBINATION FOR IMPROVING DIAGNOSTIC ACCURACY

摘要

Identifying the most suitable classifier for diagnostics is a challenging task. In addition to using domain expertise, trial and error method has been widely used to identify the most suitable classifier. Classifier fusion can be used to overcome this challenge and it has been widely known to perform better than single classifier. Classifier fusion helps in overcoming the error due to inductive bias of various classifiers. The combination rule also plays a vital role in classifier fusion, and it has not been well studied which combination rules provide the best performance during classifier fusion. In this work, we develop an approach for ensemble learning consisting of an optimized combination rule. The generalizability has been acknowledged to be a challenge for training a diverse set of classifiers, and optimal balance between bias and variance errors using the combination rule in this paper. Generalizability implies the ability of a classifier to learn the underlying model from the training data and to predict the unseen observations. In this paper, cross validation has been employed during performance evaluation of each classifier to get an unbiased performance estimate. An objective function is constructed and optimized based on the performance evaluation to achieve the optimal bias-variance balance. This function can be solved as a constrained nonlinear optimization problem. Sequential Quadratic Programming based optimization with better convergence property has been employed for the optimization. We have demonstrated the applicability of the algorithm by using support vector machine and neural networks as classifiers, but the methodology can be broadly applicable for combining other classifier algorithms as well. The method has been applied to the fault diagnosis of analog circuits. The performance of the proposed algorithm has been compared to other combination rules in the literature. It is observed that the proposed combination rule performs better in reducing the number of false positives and false negatives.