It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS). The aim of AdaboostFS is to reduce the number of features used by each base classifer and hence the overall computation required by the ensemble. To do this the algorithm combines a regularised version of Boosting AdaboostReg [1] with a floating feature search for each base classifier. AdaboostFS is compared using four benchmark data sets to AdaboostAll, which uses all features and to AdaboostRSM, which uses a random selection of features. Performance is assessed based on error rate, ensemble error and diversity, and the total number of features used for classification. Results show that AdaboostFS achieves a lower error rate and higher diversity than AdaboostAll, and achieves a lower error rate and comparable diversity to AdaboostRSM. However, over the other methods AdaboostFS produces a significant reduction in the number of features required for classification in each base classifier and the entire ensemble.

This paper presents an experimental protocol developed for the design, performance estimation and comparison of underwater video classifier systems. Such systems have to be designed using application data that is small, sparse and extremely variable. The proposed protocol uses outlier rejection, data pairing, Bootstrap performance estimation and hypothesis testing to achieve a robust performance estimate and comparison between classifier designs. The protocol is demonstrated and assessed on an application experiment. The application involves the design of a classification system for the automated detection of trawling marks from mission video. Two systems are proposed using selective and geometric feature types and an ensemble classifier. The protocol robustly identifies differences between the two proposed system designs using error and discrimination rates. Overall the geometric feature system is chosen as the final system. The protocol was also compared with other performance estimates and found to have the closest match to actual test data performance.

This paper presents a study of the Boosting Feature Selection (BFS) algorithm [1], a method which incorporates feature selection into Adaboost. Such an algorithm is interesting as it combines the methods studied by Boosting and ensemble feature selection researchers. Observations are made on generalisation, weighted error and error diversity to compare the algorithms performance to Adaboost while using a nearest mean base learner. Ensemble feature prominence is proposed as a stop criterion for ensemble construction. Its quality assessed using the former performance measures. BFS is found to compete with Adaboost in terms of performance, despite the reduced feature description for each base classifer. This is explained using weighted error and error diversity. Results show the proposed stop criterion to be useful for trading ensemble performance and complexity.