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Although data mining problems require a flat mining table as input, in many real-world applications analysts are interested in finding patterns in a relational database. To this end, new methods and software have been recently developed that automatically add attributes (or features) to a target table of a relational database which summarize information from all other tables. When attributes are automatically constructed by these methods, selecting the important attributes is particularly difficult, because a large number of the attributes are highly correlated. In this setting, attribute selection techniques such as the Least Absolute Shrinkage and Selection Operator (lasso), elastic net, and other machine learning methods tend to under-perform. In this paper, we introduce a novel attribute selection procedure, where after an initial screening step, we cluster the attributes into different groups and apply the group lasso to select both the true attributes groups and then the true attributes. The procedure is particularly suited to high dimensional data sets where the attributes are highly correlated. We test our procedure on several simulated data sets and a real-world data set from a marketing database. The results show that our proposed procedure obtains a higher predictive performance while selecting a much smaller set of attributes when compared to other state-of-the-art methods.

We are deeply concerned about how machine learning and algorithms create and perpetuate inequalities in health. We are to believe that algorithms are developed to ensure that no one will have an unfair advantage over anyone else and that human bias is removed from decisionmaking. Sounds good in theory.In real-life circumstances- such as medical diagnoses and policies that determine access to health care and social services or where your child is placed in school-algorithms can separate populations into groups of haves and have-nots along racial lines, exacerbating the racial disparity experienced by the different groups. Algorithms can determine the health of entire communities. Invisible to most of us, algorithms are described as the great equalizers.However, unlike people, all algorithms are not created equal. Scheduling a medical appointment is the most common way for patients to access a health provider: a patient asks for an appointment and is given a day and time to see a doctor. If she's on time, she expects that she'll be seen at or about the time of her appointment. Straightforward and fair? Or not? Our recent study1 argues that state-of-theart appointment scheduling algorithms may, in fact, contribute to racial disparities, because they make Black patients wait longer than non-Black patients.In our study, in which we examined electronic scheduling systems in safety net clinics, we revealed how racial bias is woven into the algorithms of electronic health records scheduling systems. To understand how this happens, consider how modern appointment scheduling systems work. To maximize efficiency, most outpatient clinics overbook some of their appointment slots, that is, they give the same appointment time to more than one patient. Overbooking is meant to ensure that providers are fully utilized even if some patients fail to show up for their scheduled appointment. However, ifpatients who are scheduled in overbooked slots do show up, some of them will experience waiting time at the clinic because the provider can see only one patient at a time.

A long-standing challenge in the metaheuristic literature is to devise a way to select parent solutions in evolutionary population-based algorithms to yield better offspring, and thus provide improved solutions to populate successive generations. We identify a way to achieve this goal that simultaneously improves the efficiency of the evolutionary process. Our strategy derives from a proposal associated with the scatter search and path relinking evolutionary algorithms that prescribes clustering the solutions and focusing on the two classes of solution combinations where the parents alternatively belong to the same cluster or to different clusters. We demonstrate the efficacy of our approach for selecting parents within this scheme by applying it to the important domain of quadratic unconstrained binary optimization (QUBO), which provides a model for solving a wide range of binary optimization problems. Within this setting, we focus on the path relinking algorithm, which together with tabu search has provided one of the most effective methods for QUBO problems. Computational tests disclose that our solution combination strategy improves the best results in the literature for hard QUBO instances.

An Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, in this case lesser access to medical care. This gives rise to one type of Accuracy-Fairness trade-off: preserve the efficiency offered by using AI to schedule appointments or discard that efficiency in order to avoid perpetuating ethno-racial disparities. Similar trade-offs arise in a range of AI applications including others in medicine, as well as in education, judicial systems, and public security, among others. This article presents a framework for addressing such trade-offs where Machine Learning and Optimization components of the algorithm are decoupled. Applied to medical appointment scheduling, our framework articulates four approaches intervening in different ways on different components of the algorithm. Each yields specific results, in one case preserving accuracy comparable to the current state-of-the-art while eliminating the disparity.

An important phase of a wind farm design is solving the Wind Farm Layout Optimization Problem (WFLOP), which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Although this problem has been receiving increasing attention from the scientific community, the existing approaches do not completely respond to the needs of a wind farm developer, mainly because they do not address construction and logistical issues. This chapter describes the WFLOP, gives an overview on the existing work, and discusses the challenges that may be overcome by future research.

Data Mining involves the extraction of new knowledge from large data sets. Despite the growing research interest in data mining, however, integrating this extra knowledge into the subsequent decision making processes has received little attention. Within the context of operations management, this integration can occur in two different ways: by providing inputs for an optimization procedure and by analyzing the output of an optimization procedure. In this dissertation, I will begin by introducing a database exploration technique, which is used to improve the drug discovery process of a pharmaceutical company (Samorani et al., 2011). The same procedure is also applied to a mental health clinic's database to predict whether patients will show up at their scheduled appointments. The knowledge obtained with this procedure is then used to improve patient scheduling procedures (Samorani and LaGanga, 2011). I will finally discuss how data mining can be used to learn useful information about the structure of a problem (Samorani and Laguna, 2012).

In this study, a layout optimization framework for offshore wind farms is proposed under widely accepted assumptions. Although wind power meets sustainable electricity standards and has less environmental impact than conventional sources, onshore wind farms currently supply only 3% of the nation's electricity. Nevertheless, onshore wind farms avoid emission of 62 million tons of carbon each year and reduce expected carbon emissions from the electricity sector by 2.5%. Due to higher wind speeds offthe coast, offshore wind farms' potential for electricity production is typically much higher than onshore counterparts. According to the U.S. Energy Information Agency, offshore wind power is relatively more expensive to construct, operate, and maintain. Considering the potential for improvement in such a large scale, we review studies from the literature that investigate the location of wind turbines within an offshore wind farm. We present a rigorous mathematical model that would minimize the cost of wind energy by examining the trade-offbetween the advantages of packing the turbines closer together and the loss generated by wake effect. [PUBLICATION ABSTRACT]