Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective

In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method to use to explain these models, with this choice being almost directly linked to the popularity of a specific method. Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with the proposal to explain black box models through global rankings of feature relevance, which based on different methodologies, generate global explanations that indicate how the model's inputs explain its predictions. In this context, 41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost, Random Forest, and Gradient Boosting), and 6 XAI methods were used to support the launch of a new XAI method, called eXirt, based on Item Response Theory - IRT and aimed at tree-ensemble black box models that use tabular data referring to binary classification problems. In the first set of analyses, the 164 global feature relevance ranks of the eXirt were compared with 984 ranks of the other XAI methods present in the literature, seeking to highlight their similarities and differences. In a second analysis, exclusive explanations of the eXirt based on Explanation-by-example were presented that help in understanding the model trust. Thus, it was verified that eXirt is able to generate global explanations of tree-ensemble models and also local explanations of instances of models through IRT, showing how this consolidated theory can be used in machine learning in order to obtain explainable and reliable models.Comment: 54 pages, 15 Figures, 3 Equations, 7 tabl

A step towards a reinforcement learning de novo genome assembler

The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems - such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including state space exploration optimization strategies based on dynamic pruning and mutual collaboration with evolutionary computing. These investigations were tried on 23 new environments with larger inputs than those used previously. All these environments are freely available on the internet for the evolution of this research by the scientific community. The results suggest consistent performance progress using the proposed improvements, however, they also demonstrate the limitations of them, especially related to the high dimensionality of state and action spaces. We also present, later, the paths that can be traced to tackle genome assembly efficiently in real scenarios considering recent, successfully reinforcement learning applications - including deep reinforcement learning - from other domains dealing with high-dimensional inputs

O CAMINHO DAS MISSÕES COMO PRODUTO TURÍSTICO DE INTEGRAÇÃO REGIONAL

O presente artigo traz uma reflexão sobre cultura e turismo, e sobre como um produto turístico pode ser um instrumento de desenvolvimento e de integração regional. O território de estudo situa-se na Região das Missões, no Rio Grande do Sul. A análise está focada no Caminho das Missões, um produto turístico baseado na roteirização entre municípios que tem como característica a integração de toda uma cadeia produtiva do turismo. Sua importância, entretanto, vai além dos benefícios econômicos, já que ao longo de suas opções de roteiros com até 338 km, trabalha com os signos culturais e religiosos da região formando um conjunto cultural que referencia a história missioneira e propicia o resgate da auto-estima de toda uma localidade. Compreende-se que o turismo, constituído nesse contexto como estratégia de integração regional requer planejamento e acompanhamento a fim de se maximizar os benefícios culturais, sociais e econômicos para a região.&nbsp

Explanation-by-Example Based on Item Response Theory

Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have characteristics that allow for self-explanation of their predictions. This situation leads researchers in the field and society to the following question: How can I trust the prediction of a model I cannot understand? In this sense, XAI emerges as a field of AI that aims to create techniques capable of explaining the decisions of the classifier to the end-user. As a result, several techniques have emerged, such as Explanation-by-Example, which has a few initiatives consolidated by the community currently working with XAI. This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. To this end, four datasets with different levels of complexity were used, and the Random Forest model was used as a hypothesis test. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable.Comment: 15 pages, 5 figures, 3 tables, submitted for the BRACIS'22 conferenc

Aplicação de Técnicas de Aprendizagem de Máquina em Objetos de Aprendizagem baseado em Software: um Mapeamento Sistemático a partir das Publicações do SBIE

Pesquisadores da área educacional buscam melhores formas de aplicação de práticas em sala de aula visando permitir uma melhor aquisição do conhecimento. Geralmente são propostas ferramentas que possam auxiliar no desenvolvimento desta área, com o auxílio de técnicas de desenvolvimento mais avançadas como é o caso da Aprendizagem de Máquina. Sendo assim, este trabalho teve como objetivo mapear de que forma as técnicas de aprendizagem de máquina são utilizadas nos objetos de aprendizagem utilizando a técnica de pesquisa de Mapeamento Sistemático. Os artigos analisados pertencem ao acervo do Simpósio Brasileiro de Informática na Educação (SBIE) abrangendo os anos de 2003 a 2012