Carla Vairetti

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

Doctor of Engineering

About

22
Publications
3,027
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218
Citations

Publications

Publications (22)
Article
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...
Article
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...
Article
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...
Article
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...
Article
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...
Article
Full-text available
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...
Article
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...
Article
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...
Article
Full-text available
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...
Chapter
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...
Article
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...
Article
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...
Article
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...
Article
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...
Article
Full-text available
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,...
Technical Report
Full-text available
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...
Conference Paper
Full-text available
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...

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Projects

Projects (4)
Project
FONDECYT regular N 1200221 (2020-2023), Theproposal considers the creation of machine learning models that are robust in the pres-ence of small changes in the data distribution. These changes can be anticipated eitherby modeling the trends and seasonal patterns present in the training set or by using arobust optimization approach, in which a pessimistic framework for the data distributionis assumed. On the other hand, the project seeks to adapt and customize predictivemodels that are robust in changing environments to business analytics applications.,UAndes Santiago Chile
Project
Related to the development and application of business analytics, machine learning and sentimental analysis algorithms. In particular in this work, optimisation formulations are proposed based on the popular computational learning method Support Vector Machines (SVM)
Project
This proposal is designed under the hypothesis that the creation of synthetic instances of the minority class is not strictly necessary when millions of samples and hundreds of explanatory variables are available. However, random undersampling has some limitations when dealing with noise, and therefore researchers and practitioners have favored the use of oversampling over random undersampling even in big data environments. Therefore, this project proposes intelligent hybrid undersampling/oversampling strategies for efficient large-scale binary classification, overcoming the main shortcomings of random undersampling and intelligent oversampling.