Lin Yueh

Equivalence-Based Reduction and Incremental Optimization for Categorical and Ordinal Classification

Categories Data Science Algorithms, Data-Mining, Optimization in Data Science

Exact mathematical programming formulations for estimating classification rules provide guarantees of empirical optimality, but their size grows rapidly with the number of predictive features and model complexity. This paper shows that interpretable classifiers can be estimated by directly maximizing empirical classification accuracy under complexity constraints, using truncation and warm-start strategies compatible with disjunctive normal form … Read more

A Generalized Voting Game for Categorical Network Choices

Categories Combinatorial Optimization, Data Science Algorithms, Game Theory Tags , , ,

This paper develops a unified game-theoretical framework for data classification and network discovery based on pairwise influences in multivariate categorical choices. Data points, interpreted as individuals, are connected through a signed weighted graph and take values according to a voting rule that aggregates the influence of attractive (friend-like) and repulsive (enemy-like) neighbors. The framework consists … Read more