Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

International audienceControl charts are among the main tools in statistical process control (SPC) and have been extensively used for monitoring industrial processes. Currently, besides the single control charts, there is an interest in the concurrent ones. These graphics are characterized by the simultaneous presence of two or more single control charts. As a consequence, the individual patterns may be mixed, hindering the identification of a non-random pattern acting in the process; this phenomenon is refered as concurrent charts. In view of this problem, our first goal is to investigate the importance of an efficient separation step for pattern recognition. Then, we compare the efficiency of different Blind Source Separation (BSS) methods in the task of unmixing concurrent control charts. Furthermore, these BSS methods are combined with shape and statistical features in order to verify the performance of each one in pattern classification. In additional, the robustness of the better approach is tested in scenarios where there are different non-randomness levels and in cases with imbalanced dataset provided to the classifier. After simulating different patterns and applying several separation methods, the results have shown that the recognition rate is widely influenced by the separation and feature extraction steps and that the selection of efficient separation methods is fundamental to achieve high classification rates

A kk-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning

Besides accuracy, recent studies on machine learning models have been addressing the question on how the obtained results can be interpreted. Indeed, while complex machine learning models are able to provide very good results in terms of accuracy even in challenging applications, it is difficult to interpret them. Aiming at providing some interpretability for such models, one of the most famous methods, called SHAP, borrows the Shapley value concept from game theory in order to locally explain the predicted outcome of an instance of interest. As the SHAP values calculation needs previous computations on all possible coalitions of attributes, its computational cost can be very high. Therefore, a SHAP-based method called Kernel SHAP adopts an efficient strategy that approximate such values with less computational effort. In this paper, we also address local interpretability in machine learning based on Shapley values. Firstly, we provide a straightforward formulation of a SHAP-based method for local interpretability by using the Choquet integral, which leads to both Shapley values and Shapley interaction indices. Moreover, we also adopt the concept of $k$-additive games from game theory, which contributes to reduce the computational effort when estimating the SHAP values. The obtained results attest that our proposal needs less computations on coalitions of attributes to approximate the SHAP values

A multi-objective optimization approach for blind source separation

Orientador: Leonardo Tomazeli DuarteDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências AplicadasResumo: Vários problemas em processamento de sinais são formulados como problemas de separação cega de fontes. Classicamente, tais problemas são resolvidos através da otimização de um critério de separação relacionado a informações sobre o conjunto de sinais fonte. No entanto, em diversas situações práticas, há mais de uma informação sobre as fontes e, consequentemente, mais de um critério de separação pode ser construído para resolver o problema. Assim, este trabalho propõe a aplicação da abordagem multiobjetivo, cuja resolução é obtida através da otimização simultânea de mais de um critério, para resolver os problemas no contexto da separação cega de fontes. Com o intuito de demonstrar a aplicabilidade desta abordagem, experimentos numéricos foram realizados de maneira a comparar as soluções obtidas através da abordagem multiobjetivo com as soluções otimizando individualmente cada critério. Os resultados sugerem que a abordagem multiobjetivo fornece soluções que, analisadas pelo tomador de decisão envolvido no problema, são melhores que as alcançadas quando apenas um critério é levado em consideração no modeloAbstract: Several problems in signal processing are formulated as blind source separation problems. Classically, these problems are solved through the optimization of a separation criterion related to the source signals. However, in many practical situations, there is more than one information about the sources and, consequently, more than one separation criterion can be built to solve the problem. Therefore, this work proposes the application of a multi-objective approach, whose resolution is achieved by simultaneous optimization of more than one criterion, to solve blind source separation problems. With the purpose of demonstrating the applicability of this approach, numerical experiments were performed in order to compare the solutions obtained by the multi-objective approach with the solutions optimizing each criterion individually. The results suggest that the multi-objective approach provides solutions that, analyzed by the decision makers involved in the problem, are better than those achieved when only one criterion is taken into account in the modelMestradoPesquisa Operacional e Gestão de ProcessosMestre em Engenharia de Produção e de Manufatura2014/27108-9FAPES

Application of multi-objective optimization to blind source separation

Several problems in signal processing are addressed by expert systems which take into account a set of priors on the sought signals and systems. For instance, blind source separation is often tackled by means of a mono-objective formulation which relies on a separation criterion associated with a given property of the sought signals (sources). However, in many practical situations, there are more than one property to be exploited and, as a consequence, a set of separation criteria may be used to recover the original signals. In this context, this paper addresses the separation problem by means of an approach based on multi-objective optimization. Differently from the existing methods, which provide only one estimate for the original signals, our proposal leads to a set of solutions that can be utilized by the system user to take his/her decision. Results obtained through numerical experiments over a set of biomedical signals highlight the viability of the proposed approach, which provides estimations closer to the mean squared error solutions compared to the ones achieved via a mono-objective formulation. Moreover, since our proposal is quite general, this work also contributes to encourage future researches to develop expert systems that exploit the multi-objective formulation in different source separation problems1316070CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP311786/2014-6; 305621/2015-72014/27108-9; 2015/16325-

A statistical approach to detect sensitive features in a group fairness setting

The use of machine learning models in decision support systems with high societal impact raised concerns about unfair (disparate) results for different groups of people. When evaluating such unfair decisions, one generally relies on predefined groups that are determined by a set of features that are considered sensitive. However, such an approach is subjective and does not guarantee that these features are the only ones to be considered as sensitive nor that they entail unfair (disparate) outcomes. In this paper, we propose a preprocessing step to address the task of automatically recognizing sensitive features that does not require a trained model to verify unfair results. Our proposal is based on the Hilber-Schmidt independence criterion, which measures the statistical dependence of variable distributions. We hypothesize that if the dependence between the label vector and a candidate is high for a sensitive feature, then the information provided by this feature will entail disparate performance measures between groups. Our empirical results attest our hypothesis and show that several features considered as sensitive in the literature do not necessarily entail disparate (unfair) results

A statistical approach to detect disparity prone features in a group fairness setting

The use of machine learning models in decision support systems with high societal impact raised concerns about unfair (disparate) results for different groups of people. When evaluating such unfair decisions, one generally relies on predefined groups that are determined by a set of features that are considered sensitive. However, such an approach is subjective and does not guarantee that these features are the only ones to be considered as sensitive nor that they entail unfair (disparate) outcomes. In this paper, we propose a preprocessing step to address the task of automatically recognizing disparity prone features that does not require a trained model to verify unfair results. Our proposal is based on the Hilbert-Schmidt independence criterion, which measures the statistical dependence of variable distributions. We hypothesize that if the dependence between the label vector and a candidate is high for a sensitive feature, then the information provided by this feature will entail disparate performance measures between groups. Our empirical results attest our hypothesis and show that several features considered as sensitive in the literature do not necessarily entail disparate (unfair) results

Multilinear model: New issues in capacity identification

International audienc

The multilinear model in multicriteria decision making: The case of 2-additive capacities and contributions to parameter identification

International audienceIn several multicriteria decision making problems, it is important to consider interactions among criteria in order to satisfy the preference relations provided by the decision maker. This can be achieved by using aggregation functions based on fuzzy measures, such as the Choquet integral and the multilinear model. Although the Choquet integral has been studied in a large number of works, one does not find the same literature with respect to the multilinear model. In this context, the contribution of this work is twofold. We first provide a formulation of the multilinear model by means of a 2-additive capacity. A second contribution lies in the problem of capacity identification. We consider a supervised approach and apply optimization models with and without regularization terms. Results obtained in numerical experiments with both synthetic and real data attest the performance of the considered approaches