Aggregation of classifiers ensemble using local discriminatory power and quantiles

• We developed new method of integrating classifiers in an ensemble based on quantiles.
• We have shown superiority of our solution on the benchmark problems.
• We have applied this solution to recognition of melanoma and proved its superiority.

The paper presents a new approach to the dynamic classifier selection in an ensemble by applying the best suited classifier for the particular testing sample. It is based on the area under curve (AUC) of the receiver operating characteristic (ROC) of each classifier. To allow application of different types of classifiers in an ensemble and to reduce the influence of outliers, the quantile representation of the signals is used. The quantiles divide the ordered data into essentially equal-sized data subsets providing approximately uniform distribution of [0–1] support for each data point. In this way the recognition problem is less sensitive to the outliers, scales and noise contained in the input attributes. The numerical results presented for the chosen benchmark data-mining sets and for the data-set of images representing melanoma and non-melanoma skin lesions have shown high efficiency of the proposed approach and superiority to the existing methods.