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Papers by Cristina Zuheros

Research paper thumbnail of An Interpretable Client Decision Tree Aggregation process for Federated Learning

arXiv (Cornell University), Apr 3, 2024

Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, ... more Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory models, are ideal for collaborative model training across multiple devices in resource-constrained environments such as federated learning environments for injecting interpretability in these models. Decision tree structure makes the aggregation in a federated learning environment not trivial. They require techniques that can merge their decision paths without introducing bias or overfitting while keeping the aggregated decision trees robust and generalizable. In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art.

Research paper thumbnail of Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges

arXiv (Cornell University), Mar 22, 2024

Social Media and Internet have the potential to be exploited as a source of opinion to enrich Dec... more Social Media and Internet have the potential to be exploited as a source of opinion to enrich Decision Making solutions. Crowd Decision Making (CDM) is a methodology able to infer opinions and decisions from plain texts, such as reviews published in social media platforms, by means of Sentiment Analysis. Currently, the emergence and potential of Large Language Models (LLMs) lead us to explore new scenarios of automatically understand written texts, also known as natural language processing. This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions. We integrate ChatGPT in CDM processes as a flexible tool that infer the opinions expressed in texts, providing numerical or linguistic evaluations where the decision making models are based on the prompt design strategies. We include a multicriteria decision making scenario with a category ontology for criteria. We also consider ChatGPT as an end-to-end CDM model able to provide a general opinion and score on the alternatives. We conduct empirical experiments on real data extracted from TripAdvisor, the TripR-2020Large dataset. The analysis of results show a promising branch for developing quality decision making models using ChatGPT. Finally, we discuss the challenges of consistency, sensitivity and explainability associated to the use of LLMs in CDM processes, raising open questions for future studies.

Research paper thumbnail of An Online A/B Testing Decision Support System for Web Usability Assessment Based on a Linguistic Decision-Making Model

Research paper thumbnail of Flex: Flexible Federated Learning Framework

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processin... more In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications.

Research paper thumbnail of Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery

Information Fusion, Sep 1, 2023

Research paper thumbnail of Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews

arXiv (Cornell University), Jul 31, 2020

Decision making models are constrained by taking the expert evaluations with pre-defined numerica... more Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multiperson Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via

Research paper thumbnail of Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid

arXiv (Cornell University), Jul 31, 2020

Decision making models are constrained by taking the expert evaluations with pre-defined numerica... more Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multiperson Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via

Research paper thumbnail of Computing with Words: Revisiting the Qualitative Scale

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Dec 1, 2018

Computing with Words (CW) is a methodology which takes an essential role in decision making probl... more Computing with Words (CW) is a methodology which takes an essential role in decision making problems, thus the first aim of this paper is to highlight the importance of CW in decision making. There are many proposals of CW models that can be classified in two main fields: approaches based on membership functions and approaches based on qualitative scales. This paper focuses on the qualitative scales since it provides a closer environment to human knowledge and more accurate results avoiding a loss of information. An analysis and discussion of the qualitative scales of linguistic values in CW for decision making is provided. Finally, we highlight the open challenges for the qualitative scales in linguistic decision making.

Research paper thumbnail of Red neural recurrente para la desambiguación de entidades en datos de medios sociales

Research paper thumbnail of Design and consensus content validity of the questionnaire for b-learning education: A 2-Tuple Fuzzy Linguistic Delphi based Decision Support Tool

Research paper thumbnail of Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery

Research paper thumbnail of Managing minority opinions in large-scale group decision making based on community detection and group polarization

Computers & Industrial Engineering

Research paper thumbnail of Crowd Decision Making: Sparse Representation Guided by Sentiment Analysis for Leveraging the Wisdom of the Crowd

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Research paper thumbnail of Sentiment Analysis based Multi-Person Multi-criteria Decision Making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews

Information Fusion, 2021

Decision making models are constrained by taking the expert evaluations with pre-defined numerica... more Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multiperson Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via

Research paper thumbnail of Computing with Words: Revisiting the Qualitative Scale

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018

Computing with Words (CW) is a methodology which takes an essential role in decision making probl... more Computing with Words (CW) is a methodology which takes an essential role in decision making problems, thus the first aim of this paper is to highlight the importance of CW in decision making. There are many proposals of CW models that can be classified in two main fields: approaches based on membership functions and approaches based on qualitative scales. This paper focuses on the qualitative scales since it provides a closer environment to human knowledge and more accurate results avoiding a loss of information. An analysis and discussion of the qualitative scales of linguistic values in CW for decision making is provided. Finally, we highlight the open challenges for the qualitative scales in linguistic decision making.

Research paper thumbnail of Deep recurrent neural network for geographical entities disambiguation on social media data

Knowledge-Based Systems, 2019

A particular challenge in Natural Language Processing is the disambiguation of polysemic words. T... more A particular challenge in Natural Language Processing is the disambiguation of polysemic words. The great availability, diversity and the speed of changing of the data from on-line sources force the development of disambiguation systems with a reduced dependency on linguistic resources. We argue that the contextual neural encoding of a specific entity avoids the need of using external linguistic resources like knowledge bases. Hence, we propose a neural network architecture grounded in the use of Long Short-Term Memory Recurrent Neural Network for encoding the context of a target geographical entity, specifically Two k-Contextual Windows model for the disambiguation of the geographical entity Granada. We generate two annotated corpora of texts from social media written in English and Spanish, which we use to evaluate our proposal. The results show that our claim holds.

Research paper thumbnail of An Interpretable Client Decision Tree Aggregation process for Federated Learning

arXiv (Cornell University), Apr 3, 2024

Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, ... more Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory models, are ideal for collaborative model training across multiple devices in resource-constrained environments such as federated learning environments for injecting interpretability in these models. Decision tree structure makes the aggregation in a federated learning environment not trivial. They require techniques that can merge their decision paths without introducing bias or overfitting while keeping the aggregated decision trees robust and generalizable. In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art.

Research paper thumbnail of Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges

arXiv (Cornell University), Mar 22, 2024

Social Media and Internet have the potential to be exploited as a source of opinion to enrich Dec... more Social Media and Internet have the potential to be exploited as a source of opinion to enrich Decision Making solutions. Crowd Decision Making (CDM) is a methodology able to infer opinions and decisions from plain texts, such as reviews published in social media platforms, by means of Sentiment Analysis. Currently, the emergence and potential of Large Language Models (LLMs) lead us to explore new scenarios of automatically understand written texts, also known as natural language processing. This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions. We integrate ChatGPT in CDM processes as a flexible tool that infer the opinions expressed in texts, providing numerical or linguistic evaluations where the decision making models are based on the prompt design strategies. We include a multicriteria decision making scenario with a category ontology for criteria. We also consider ChatGPT as an end-to-end CDM model able to provide a general opinion and score on the alternatives. We conduct empirical experiments on real data extracted from TripAdvisor, the TripR-2020Large dataset. The analysis of results show a promising branch for developing quality decision making models using ChatGPT. Finally, we discuss the challenges of consistency, sensitivity and explainability associated to the use of LLMs in CDM processes, raising open questions for future studies.

Research paper thumbnail of An Online A/B Testing Decision Support System for Web Usability Assessment Based on a Linguistic Decision-Making Model

Research paper thumbnail of Flex: Flexible Federated Learning Framework

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processin... more In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications.

Research paper thumbnail of Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery

Information Fusion, Sep 1, 2023

Research paper thumbnail of Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews

arXiv (Cornell University), Jul 31, 2020

Decision making models are constrained by taking the expert evaluations with pre-defined numerica... more Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multiperson Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via

Research paper thumbnail of Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid

arXiv (Cornell University), Jul 31, 2020

Decision making models are constrained by taking the expert evaluations with pre-defined numerica... more Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multiperson Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via

Research paper thumbnail of Computing with Words: Revisiting the Qualitative Scale

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Dec 1, 2018

Computing with Words (CW) is a methodology which takes an essential role in decision making probl... more Computing with Words (CW) is a methodology which takes an essential role in decision making problems, thus the first aim of this paper is to highlight the importance of CW in decision making. There are many proposals of CW models that can be classified in two main fields: approaches based on membership functions and approaches based on qualitative scales. This paper focuses on the qualitative scales since it provides a closer environment to human knowledge and more accurate results avoiding a loss of information. An analysis and discussion of the qualitative scales of linguistic values in CW for decision making is provided. Finally, we highlight the open challenges for the qualitative scales in linguistic decision making.

Research paper thumbnail of Red neural recurrente para la desambiguación de entidades en datos de medios sociales

Research paper thumbnail of Design and consensus content validity of the questionnaire for b-learning education: A 2-Tuple Fuzzy Linguistic Delphi based Decision Support Tool

Research paper thumbnail of Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery

Research paper thumbnail of Managing minority opinions in large-scale group decision making based on community detection and group polarization

Computers & Industrial Engineering

Research paper thumbnail of Crowd Decision Making: Sparse Representation Guided by Sentiment Analysis for Leveraging the Wisdom of the Crowd

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Research paper thumbnail of Sentiment Analysis based Multi-Person Multi-criteria Decision Making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews

Information Fusion, 2021

Decision making models are constrained by taking the expert evaluations with pre-defined numerica... more Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multiperson Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via

Research paper thumbnail of Computing with Words: Revisiting the Qualitative Scale

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018

Computing with Words (CW) is a methodology which takes an essential role in decision making probl... more Computing with Words (CW) is a methodology which takes an essential role in decision making problems, thus the first aim of this paper is to highlight the importance of CW in decision making. There are many proposals of CW models that can be classified in two main fields: approaches based on membership functions and approaches based on qualitative scales. This paper focuses on the qualitative scales since it provides a closer environment to human knowledge and more accurate results avoiding a loss of information. An analysis and discussion of the qualitative scales of linguistic values in CW for decision making is provided. Finally, we highlight the open challenges for the qualitative scales in linguistic decision making.

Research paper thumbnail of Deep recurrent neural network for geographical entities disambiguation on social media data

Knowledge-Based Systems, 2019

A particular challenge in Natural Language Processing is the disambiguation of polysemic words. T... more A particular challenge in Natural Language Processing is the disambiguation of polysemic words. The great availability, diversity and the speed of changing of the data from on-line sources force the development of disambiguation systems with a reduced dependency on linguistic resources. We argue that the contextual neural encoding of a specific entity avoids the need of using external linguistic resources like knowledge bases. Hence, we propose a neural network architecture grounded in the use of Long Short-Term Memory Recurrent Neural Network for encoding the context of a target geographical entity, specifically Two k-Contextual Windows model for the disambiguation of the geographical entity Granada. We generate two annotated corpora of texts from social media written in English and Spanish, which we use to evaluate our proposal. The results show that our claim holds.