Abstract
We address a classification problem where data are not single-valued, but distributions. The objective is to identify Internet traffic re-direction. Each observation consists of a block of 10 measurements of round-trip-times (RTT) measured at each of a set of probes, and is represented by the corresponding empirical distribution. The proposed approach relies on a method for discriminant analysis of distributional data that uses fractional programming, and where distributions are represented by quantile functions, under specific assumptions. A linear discriminant function is defined, that allows obtaining a score for each unit, in the form of a quantile function. This is then used to classify the units in a priori groups, using the Mallows distance. Results show that proposed approach works well, allowing for the identification of the diverted traffic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The textile industry, a vital sector in global production, relies heavily on dyeing processes to meet stringent quality and consistency standards. This study addresses the challenge of identifying and mitigating non-conformities in dyeing patterns, such as stains, fading and coloration issues, through advanced data analysis and machine learning techniques. The authors applied Random Forest and Gradient Boosted Trees algorithms to a dataset provided by a Portuguese textile company, identifying key factors influencing dyeing non-conformities. Our models highlight critical features impacting non-conformities, offering predictive capabilities that allow for preemptive adjustments to the dyeing process. The results demonstrate significant potential for reducing non-conformities, improving efficiency and enhancing overall product quality.

Abstract

Abstract

Abstract