No abstract

In this paper, a novel approach is proposed to analyze and interpret consumer preference data expressed as weak orderings (ie, preference chains) over a set of alternatives. A simple distance metric based on cosine similarity is used to assess the repeatability and reproducibility of consumer preferences. These concepts have been borrowed from metrology and can be usefully adopted to evaluate the meaningfulness of aggregated measures (eg, median preference chain) defined over the preference data set. The proposed approach is fully exploited through a real case study involving a group of consumers expressing their preferences over alternative types of pizza toppings.

A major issue in classification problems arises when dealing with class imbalance, which requires the adoption of a suitable performance measure able to handle imbalanced data sets. This paper introduces the Balanced 𝐴𝐶 1 and its weighted version Balanced 𝐴𝐶 2 as classifier performance measures suitable for both balanced and imbalanced data sets. The performances of the proposed measures are compared against those of other well-known performance measures through an empirical comparison using several algorithms and data sets.Moreover, the applicability of Balanced 𝐴𝐶 1 is showcased through an illustrative example dealing with steel plate faults classifications, where class imbalance typically occurs due to non-common defects which, though rare, may seriously impact steel quality.