Carla Vairetti

University of the Andes (Chile) | UANDES · Faculty of Engineering and Applied Sciences

In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast...

In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators, leveraging the power of f...

As in any other task, the process of building machine learning models can benefit from prior experience. Meta-learning for classifier selection gains knowledge from characteristics of different datasets and/or previous performance of machine learning techniques to make better decisions for the current modeling process. Meta-learning approaches firs...

Dataset shift is a relevant topic in unsupervised learning since many applications face evolving environments, causing an important loss of generalization and performance. Most techniques that deal with this issue are designed for data stream clustering, whose goal is to process sequences of data efficiently under Big Data. In this study, we claim...

It is of utmost importance for marketing academics and service industry practitioners to understand the factors that influence customer satisfaction. This study proposes a novel framework to analyze open-ended survey data and extract drivers of customer satisfaction. This is done automatically via deep learning models for natural language processin...

Debt collection is a very important business application of predictive analytics. This task consists of foreseeing repayment chances of late payers. In this sense, contact centers have a central role in debt collection since it improves profitability by turning monetary losses into a direct benefit to banks and other financial institutions. In this...

The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known resampling strategy that has been successfully used for dealing with the class-imbalance problem, one of the most challenging pattern recognition tasks in the last two decades. In this work, we claim that SMOTE has an important issue when defining the neighborhood in order to cr...

In this paper, we present a novel approach for n-gram generation in text classification. The a-priori algorithm is adapted to prune word sequences by combining three feature selection techniques. Unlike the traditional two-step approach for text classification in which feature selection is performed after the n-gram construction process, our propos...

Over the last two decades, governments have increased their investment in information technology to improve the use of public resources, using public electronic procurement systems to obtain better prices, better solutions and to show transparency in the procurement process. Public procurement of software development projects is specific acquisitio...

Traffic pumping is a type of fraud committed in several countries, in which small telephone operators inflate the number of incoming calls to their networks, profiting from a higher access charge in relation to the network operator associated with the origin of the call. The identification of traffic pumping is complex due to the lack of labels for...

The predictive performance of classification methods relies heavily on the nature of the environment, as in the joint distribution of inputs and outputs may evolve over time. This issue is known as dataset shift. Given that most statistical and machine learning techniques assume that the training sample is drawn from the same distribution as the te...

Este artigo propõe o projeto de uma plataforma para apoiar o projeto de políticas públicas para o transporte de cargas. O foco está no que é chamado de “última milha portuária” ou interface terrestre dos portos na área interportuária, também conhecida como transporte drayage. O porto de San Antonio será considerado como estudo de caso. Os resultado...

Foreign trade in Chile accounts for approximately 30% of the GDP (gross domestic product). In 2015, from the total volume, 92% of the imports and 96% of the exports were transferred by maritime ports. Hence, maritime ports are key nodes of the global transport chain with a strategic role on the country’s economic development. The export and import...

Inter-Organizational Information Systems (IOISs) for seaport logistics facilitate monitoring operations, the exchange of information with stakeholders, and meeting regulations of foreign trade. However, seaport contexts entail complexities in terms of stakeholder involvement and business processes that must be considered thoroughly toward the succe...

In this paper, we propose three novel profit-driven strategies for churn prediction. Our proposals extend the ideas of the Minimax Probability Machine, a robust optimization approach for binary classification that maximizes sensitivity and specificity using a probabilistic setting. We adapt this method and other variants to maximize the profit of a...

Service composition is one of the principles of service-oriented architecture; it enables reuse and allows developers to combine existing services in order to create new services that in turn can be part of another composition. Dynamic composition requires that service components are chosen from a set of services with equal or similar functionality...

In this work, the Synthetic Minority Over-sampling Technique (SMOTE) approach is adapted for high-dimensional binary settings. A novel distance metric is proposed for the computation of the neighborhood for each minority sample, which takes into account only a subset of the available attributes that are relevant for the task. Three variants for the...

Dynamic Web services composition aims to generate a composition plan at run-time. Semantic-based techniques rely on annotating services to facilitate the discovery of the service components that satisfy a user need (matchmaking). The matchmaking process places most attention on service selection rather than on the behaviour of the composed service,...

The effective delivery of services in electronic government is a critical feature and also a difficult challenge. Contrasting with commercial systems, electronic government systems must be directed to the broadest possible spectrum of consumers, lest their value as tools to improve and modernise democracies can be hindered. Furthermore, since in th...

Complex applications, in particular Web applications, deal with a myriad of different concerns and some of them affect several others. The result is that these crosscutting concerns are scattered throughout different software artifacts and tangled with other concerns. In this paper we present an approach for modeling and composing navigational conc...