Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy

Feature selection with simple ANN ensembles

Feature selection is a well-known pre-processing technique, commonly used with high-dimensional datasets. Its main goal is to discard useless or redundant variables, reducing the dimensionality of the input space, in order to increase the performance and interpretability of models. In this work we introduce the ANN-RFE, a new technique for feature selection that combines the accurate and time-e cient RFE method with the strong discrimination capabilities of ANN ensembles. In particular, we discuss two feature importance metrics that can be used with ANN-RFE: the shu ing and dE metrics. We evaluate the new method using an arti cial example and ve real-world wide datasets, including gene-expression data. Our results suggest that both metrics have equivalent capabilities for the selection of informative variables. ANNRFE seems to produce overall results that are equivalent to previous e cient methods, but can be more accurate on particular datasets.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI