Classification performance metric elicitation and its applications

Given a learning problem with real-world tradeoffs, which cost function should the model be trained to optimize? This is the metric selection problem in machine learning. Despite its practical interest, there is limited formal guidance on how to select metrics for machine learning applications. This thesis outlines metric elicitation as a principled framework for selecting the performance metric that best reflects implicit user preferences. Once specified, the evaluation metric can be used to compare and train models. In this manuscript, we formalize the problem of Metric Elicitation and devise novel strategies for eliciting classification performance metrics using pairwise preference feedback over classifiers. Specifically, we provide novel strategies for eliciting linear and linear-fractional metrics for binary and multiclass classification problems, which are then extended to a framework that elicits group-fair performance metrics in the presence of multiple sensitive groups. All the elicitation strategies that we discuss are robust to both finite sample and feedback noise, thus are useful in practice for real-world applications. Using the tools and the geometric characterizations of the feasible confusion statistics space from the binary, multiclass, and multiclass-multigroup classification setups, we further provide strategies to elicit from a wider range of complex, modern multiclass metrics defined by quadratic functions of predictive rates by exploiting their local linear structure. This strategy can then be easily extended to eliciting metrics of higher order polynomials. From application perspective, we also propose to use the metric elicitation framework in optimizing complex black box metrics that is amenable to deep network training. In particular, the linear elicitation strategies can be used to elicit local-linear approximation of the black-box metrics, which are then exploited by existing iterative optimization routines. Lastly, to bring theory closer to practice, we conduct a preliminary real-user study that shows the efficacy of the metric elicitation framework in recovering the users' preferred performance metric in a binary classification setup

Towards emotional interaction: using movies to automatically learn users’ emotional states

The HCI community is actively seeking novel methodologies to gain insight into the user's experience during interaction with both the application and the content. We propose an emotional recognition engine capable of automatically recognizing a set of human emotional states using psychophysiological measures of the autonomous nervous system, including galvanic skin response, respiration, and heart rate. A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies' scenes selected to induce emotions ranging from the positive to the negative valence dimension, including happiness, anger, disgust, sadness, and fear. In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.info:eu-repo/semantics/publishedVersio

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

Evaluation of brain functional connectivity from electroencephalographic signals under different emotional states

The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.- This publication is part of the R&D Projects Nos. PID2020-115220RB-C21, EQC2019-006063P, funded by MCIN/AEI/10.13039/501100011033/, and 2018/11744, funded by "ERDF A way to make Europe". This work was partially supported by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz Garcia-Martinez holds FPU16/03740 scholarship from Spanish Ministerio de Educacion y Formacion Profesional

Autonomous Assessment of Videogame Difficulty Using Physiological Signals

Given the well-explored relation between challenge and involvement in a task, (e.g., as described in Csikszentmihalyi’s theory of flow), it could be argued that the presence of challenge in videogames is a core element that shapes player experiences and should, therefore, be matched to the player’s skills and attitude towards the game. However, handling videogame difficulty, is a challenging problem in game design, as too easy a task can lead to boredom and too hard can lead to frustration. Thus, by exploring the relationship between difficulty and emotion, the current work intends to propose an artificial intelligence model that autonomously predicts difficulty according to the set of emotions elicited in the player. To test the validity of this approach, we developed a simple puzzle-based Virtual Reality (VR) videogame, based on the Trail Making Test (TMT), and whose objective was to elicit different emotions according to three levels of difficulty. A study was carried out in which physiological responses as well as player self- reports were collected during gameplay. Statistical analysis of the self-reports showed that different levels of experience with either VR or videogames didn’t have a measurable impact on how players performed during the three levels. Additionally, the self-assessed emotional ratings indicated that playing the game at different difficulty levels gave rise to different emotional states. Next, classification using a Support Vector Machine (SVM) was performed to verify if it was possible to detect difficulty considering the physiological responses associated with the elicited emotions. Results report an overall F1-score of 68% in detecting the three levels of difficulty, which verifies the effectiveness of the adopted methodology and encourages further research with a larger dataset.Dada a relação bem explorada entre desafio e envolvimento numa tarefa (p. ex., con- forme descrito na teoria do fluxo de Csikszentmihalyi), pode-se argumentar que a pre- sença de desafio em videojogos é um elemento central que molda a experiência do jogador e deve, portanto, ser compatível com as habilidades e a atitude que jogador exibe perante o jogo. No entanto, saber como lidar com a dificuldade de um videojogo é um problema desafiante no design de jogos, pois uma tarefa muito fácil pode gerar tédio e muito di- fícil pode levar à frustração. Assim, ao explorar a relação entre dificuldade e emoção, o presente trabalho pretende propor um modelo de inteligência artificial que preveja de forma autônoma a dificuldade de acordo com o conjunto de emoções elicitadas no jogador. Para testar a validade desta abordagem, desenvolveu-se um jogo de puzzle em Realidade Virtual (RV), baseado no Trail Making Test (TMT), e cujo objetivo era elicitar diferentes emoções tendo em conta três níveis de dificuldade. Foi realizado um estudo no qual se recolheram as respostas fisiológicas, juntamente com os autorrelatos dos jogado- res, durante o jogo. A análise estatística dos autorelatos mostrou que diferentes níveis de experiência com RV ou videojogos não tiveram um impacto mensurável no desempenho dos jogadores durante os três níveis. Além disso, as respostas emocionais auto-avaliadas indicaram que jogar o jogo em diferentes níveis de dificuldade deu origem a diferentes estados emocionais. Em seguida, foi realizada a classificação por intermédio de uma Má- quina de Vetores de Suporte (SVM) para verificar se era possível detectar dificuldade, considerando as respostas fisiológicas associadas às emoções elicitadas. Os resultados re- latam um F1-score geral de 68% na detecção dos três níveis de dificuldade, o que verifica a eficácia da metodologia adotada e incentiva novas pesquisas com um conjunto de dados maior

Tourist experiences recommender system based on emotion recognition with wearable data

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.This research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja. We thank the members of the GICAC group (Research Group in Administrative and Accounting Sciences) of the Universidad Santo Tomás Seccional Tunja for their participation in the experimental phase of this investigation