Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of...

Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, t...

Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes without employing a search strategy. The...

What simple learning rules can allow agents to cope with changing environments? We tested whether a rule that neglects base rates of events in the world — something that is usually considered irrational — could be as successful as Bayesian inference — the usual standard of rationality — in making cuebased predictions about events in time-varying environments. We focused on environments in which...

Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, t...

We introduce a novel generative model for interpretable subgroup analysis for causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small set of short rules to capture a subgroup where the average treatment effect is elevated compared to the entire population. We present a Bayesian framework for learning a causal rule set. The Bayesian framework consists of a prior that favo...

The paper undertakes a conceptual scrutiny of Bayes’ rule in terms of the nature and interpretation of its probabilistic components, which reveals that there is nothing obvious or self-evident about it. First, it is not an instantiation of the conditional probability formula because (i) it involves conditioning on the unobservable event (a hypothesis), and (ii) it ignores the gap between Plato...

We consider the performance and incentive compatibility of voting rules in a Bayesian environment with independent private values and at least three alternatives. It is shown that every (ex-ante) Pareto efficient ordinal rule among neutral rules is incentive compatible under a symmetry assumption on alternatives. Furthermore, we prove that there exists an incentive compatible cardinal rule whic...

In this paper, we consider a model of valuing callable financial securities when the underlying asset price dynamic is modeled by a regime switching process. The callable securities enable both an issuer and an investor to exercise their rights to call. We show that such a model can be formulated as a coupled stochastic game for the optimal stopping problem with two sopping boundaries. We provi...

The Ellsberg paradox demonstrates that people’s belief over uncertain events might not be representable by subjective probability. We argue that Uncertainty Aversion may be viewed as a case of “Rule Rationality”. This paradigm claims that people’s decision making has evolved to simple rules that perform well in most regular environments. Such an environment consists of replicas of some basic si...