Abstract
Fusing classifiers’ decisions can improve the performance of a pattern recognition system. Many applications areas have adopted the methods of multiple classifier fusion to increase the classification accuracy in the recognition process. From fully considering the classifier performance differences and the training sample information, a multiple classifier fusion algorithm using weighted decision templates is proposed in this paper. The algorithm uses a statistical vector to measure the classifier’s performance and makes a weighed transform on each classifier according to the reliability of its output. To make a decision, the information in the training samples around an input sample is used by thek-nearest-neighbor rule if the algorithm evaluates the sample as being highly likely to be misclassified. An experimental comparison was performed on 15 data sets from the KDD’99, UCI, and ELENA databases. The experimental results indicate that the algorithm can achieve better classification performance. Next, the algorithm was applied to cataract grading in the cataract ultrasonic phacoemulsification operation. The application result indicates that the proposed algorithm is effective and can meet the practical requirements of the operation.
Highlights
A single classifier was always used in a traditional pattern recognition system
In recent years, it has been found that the samples that were wrongly classified by distinct classifiers were usually not the same in many experiments. This finding means that complementary information about the object to be recognized can be potentially offered by different classifiers and effective fusion of the complementary information is expected to considerably improve the performance of a pattern recognition system
When the member classifiers are diverse or complementary, multiple classifier systems can usually obtain higher classification accuracies compared with a single classifier [1]
Summary
Fusing classifiers’ decisions can improve the performance of a pattern recognition system. Many applications areas have adopted the methods of multiple classifier fusion to increase the classification accuracy in the recognition process. From fully considering the classifier performance differences and the training sample information, a multiple classifier fusion algorithm using weighted decision templates is proposed in this paper. The information in the training samples around an input sample is used by the k-nearest-neighbor rule if the algorithm evaluates the sample as being highly likely to be misclassified. The experimental results indicate that the algorithm can achieve better classification performance. The application result indicates that the proposed algorithm is effective and can meet the practical requirements of the operation
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