This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but…

As the violence in the Mexican drug war escalates, a proliferation of social media sites about drug trafficking in Mexico was followed by the murder of some of their users, and the eventual disappearance of many of those sites. Despite these events, there still exist a number of drug-dealing related social media outlets in this country with a…

Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…

The authors consider the problem of classifying an unknown observation into 1 of several populations by using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for classification procedures that can be used regardless of the group-conditional…

Methodological issues of using a class of neural networks called Mixture Density Networks (MDN) for discriminant analysis are discussed. MDN models have the advantage of having a rigorous probabilistic interpretation, and they have proven to be a viable alternative as a classification procedure in discrete domains. Both classification and…

There are two distinct but related purposes for carrying out a "discriminant analysis": (1) discrimination, and (2) classification. The primary objective of this paper was to review the outputs of selected computer programs often used to carry out a "discriminant analysis" with respect to these two purposes. Information provided by the programs on…

Interprets College Interest Inventory, administered to 284 students, both ways, with neither method proving superior. Suggests that under restricted conditions, discriminant function might be more appropriate for counseling purposes than configural analysis. (Author/CJ)