A postpruning decision algorithm based on loss minimization

Decision Rule-based Algorithm for Ordinal Classification based on Rank Loss Minimization

Many classification problems have in fact an ordinal nature, i.e., the class labels are ordered. We introduce a decision rule algorithm, called RankRules, tailored for this type of problems, that is based on minimization of the rank loss. In general, the complexity of the rank loss minimization is quadratic with respect to the number of training examples, however, we show that the introduced al...

Software Clustering based on Information Loss Minimization

The majority of the algorithms in the software clustering literature utilize structural information in order to decompose large software systems. Other approaches, such as using £le names or ownership information, have also demonstrated merit. However, there is no intuitive way to combine information obtained from these two different types of techniques. In this paper, we present an approach th...

Superset Learning Based on Generalized Loss Minimization

In standard supervised learning, each training instance is associated with an outcome from a corresponding output space (e.g., a class label in classification or a real number in regression). In the superset learning problem, the outcome is only characterized in terms of a superset—a subset of candidates that covers the true outcome but may also contain additional ones. Thus, superset learning ...

Evaluation of an Energy Loss-Minimization Algorithm for EVs Based on Induction Motor

Learning Decision Trees for Loss Minimization in Multi-Class Problems

Many machine learning applications require classi ers that minimize an asymmetric loss function rather than the raw misclassi cation rate. We study methods for modifying C4.5 to incorporate arbitrary loss matrices. One way to incorporate loss information into C4.5 is to manipulate the weights assigned to the examples from di erent classes. For 2-class problems, this works for any loss matrix, b...