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Explainable agglomerative clustering tree: ExACt
No full textLAUREA MAGISTRALEI modelli di Machine Learning (ML) sono sempre più al centro delle nostre attività decisionali, portando la necessità e l'attenzione sull'avere risultati spiegabili e comprensibili dal punto di vista umano.
In molte applicazioni, dalla sanità ai sistemi di rilevamento di frodi fino alla conformità normativa, avere risultati interpretabili da un umano è di fondamentale importanza per stabilire e mantenere la fiducia. Ad esempio, in ambito finanziario, se un sistema blocca una determinata transazione, le motivazioni dietro questa scelta devono essere chiare.
Molti degli studi in letteratura mirano ad affrontare l'interpretabilità di sistemi supervised come le reti neurali, mentre la spiegabilità del clustering è spesso trascurata.
In questa tesi si propone ExACt (Explainable Agglomerative Clustering Tree): un algoritmo basato sugli alberi decisionali che sfrutta la struttura del dendrogramma dell'agglomerative clustering e utilizza algoritmi di generazione di alberi decisionali per fornire spiegazioni significative, sfruttando la loro natura intrinsecamente spiegabile.
ExACt costruisce un albero decisionale per ogni livello del dendrogramma per poi semplificarlo rispetto al clustering originale ed ottenere una soluzione più compatta. L'idea chiave è che, attraversando la struttura del dendrogramma, i cluster avranno forme più semplici, permettendo la generazione di divisioni parallele agli assi più significative. Alcune di queste divisioni saranno semplificate rispetto al clustering originale per ottenere un albero più compatto.
Definiamo inoltre una nuova metrica che bilancia l'accuratezza con la semplicità dell'albero prodotto come output, chiamata "explainability index". Quest'ultima viene usata in ExACt per scegliere l'albero migliore tra quelli prodotti ai vari livelli del dendrogramma.
Testiamo la soluzione finale sia su dataset reali che sintetici, mostrando i benefici rispetto agli algoritmi di alberi decisionali standard.
Infine, applichiamo ExACt a uno scenario reale nel mondo dell'antiriciclaggio (Anti Money Laundering): scovare e spiegare il fenomeno delle cosiddette "società cartiere", ovvero società esistenti solo sulla carta e che presentano caratteristiche di bilancio simili tra loro.
Con ExACt offriamo uno strumento di analisi esplorativa che aiuta a spiegare l'assegnazione dei cluster e a migliorare la fiducia complessiva nel framework.As Machine Learning models (ML) continue to be at the epicenter of many decision-making tasks, the need for explainable and human-comprehensible results has never been more important.
In many real world applications such as healthcare, fraud detection and regulatory compliance, having human-interpretable results is of the utmost importance in building and maintaining trust. For instance, in finance if a system prohibits a transaction there has to be clear and obvious reasons behind it.
While numerous studies have been proposed to improve the interpretability of supervised models such as artificial neural network, explainability in clustering tasks is often left behind.
In this thesis we present ExACt (Explainable Agglomerative Clustering Tree): a decision tree-based algorithm that builds upon the dendrogram structure from agglomerative clustering and leverages other decision tree algorithms to provide meaningful explanations, taking advantage of their interpretable by design status.
ExACt builds a decision tree for every level of the dendrogram and then simplifies it with respect to the original clustering to obtain a more compact solution. The key idea is that by traversing the dendrogram structure, clusters will have simpler shapes that allow for more meaningful axis-parallel splits. Later, the simplification of these splits will lead to more compact solutions.
We also define an explainability metric that balances fit versus compactness of the output tree called the "explainability index", that helps our algorithm in selecting the most optimal tree.
We test the final solution on both real and synthetic datasets showing the benefits of our approach compared to standard tree building algorithms.
Finally we try ExACt in a real world scenario of regulatory compliance and Anti Money Laundering (AML): finding and explaining the "paper companies" phenomenon, meaning companies that are just so on paper that present similar characteristics in their financial statements.
With ExACt, we offer an exploratory tool to help explain cluster assignments and to improve the overall trust in the clustering framework
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Apprentissage profond pour les applications interface cerveau-machine : exploiter les avancées récentes en vision par ordinateur
No full textBCI systems enable direct communication between brain activity and external devices, with applications in assistive technology, neurorehabilitation, and human-computer interaction. Despite their potential, EEG-based BCI remain mostly confined to laboratory prototypes. Their main limitation is that neuronal dynamics captured by EEG exhibit low signal-to-noise ratio and strong variability across subjects, sessions, recording settings, and tasks, making stable statistical modeling challenging. These difficulties have led to numerous EEG-BCI-specific deep learning methods. While deep learning excels in transfer learning, its impact on BCI remains limited due to the lack of clear theoretical foundations and rigorous evaluations. This dissertation explores whether existing techniques from leading deep learning fields can be adapted to EEG decoding instead of designing new BCIspecific methods. Our findings show how deep learning advances can enhance EEG-based BCIs, improving adaptability, efficiency, and generalization across electrode configurations and tasks, while reducing calibration time.Les ICM permettent une communication directe entre l’activité cérébrale et des dispositifs externes, avec des applications en technologie d’assistance, neuro-rééducation et interaction homme-machine. Malgré leur potentiel,les ICM basées sur l’EEG restent peu utilisées en pratique, la plupart étant confinées aux laboratoires. Leur principale limite est que la dynamique neuronale capturée par l’EEG présente un faible rapport signal/bruit et une forte variabilité entre sujets, sessions et configurations d’enregistrement, rendant difficile l’apprentissage de modèles statistiques stables. Les défis des ICM-EEG ont mené au développement de nombreuses méthodes d’apprentissage profond dédiées. Pourtant, malgré ses capacités de transfert, l’apprentissage profond peine à s’imposer en ICM, faute de bases théoriques claires et d’évaluations rigoureuses. Cette thèse explore si les techniques existantes des principaux domaines de l’apprentissage profond peuvent être adaptées au décodage EEG plutôt que de concevoir de nouvelles méthodes spécifiques aux ICM. Nos résultats montrent comment les avancées en apprentissage profond peuvent améliorer les ICM en les rendant plus efficaces, avec une calibration réduite et une meilleure généralisation aux configurations d’électrodes et aux tâches
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Apprentissage profond pour les applications interface cerveau-machine : exploiter les avancées récentes en vision par ordinateur
No full textBCI systems enable direct communication between brain activity and external devices, with applications in assistive technology, neurorehabilitation, and human-computer interaction. Despite their potential, EEG-based BCI remain mostly confined to laboratory prototypes. Their main limitation is that neuronal dynamics captured by EEG exhibit low signal-to-noise ratio and strong variability across subjects, sessions, recording settings, and tasks, making stable statistical modeling challenging. These difficulties have led to numerous EEG-BCI-specific deep learning methods. While deep learning excels in transfer learning, its impact on BCI remains limited due to the lack of clear theoretical foundations and rigorous evaluations. This dissertation explores whether existing techniques from leading deep learning fields can be adapted to EEG decoding instead of designing new BCIspecific methods. Our findings show how deep learning advances can enhance EEG-based BCIs, improving adaptability, efficiency, and generalization across electrode configurations and tasks, while reducing calibration time.Les ICM permettent une communication directe entre l’activité cérébrale et des dispositifs externes, avec des applications en technologie d’assistance, neuro-rééducation et interaction homme-machine. Malgré leur potentiel,les ICM basées sur l’EEG restent peu utilisées en pratique, la plupart étant confinées aux laboratoires. Leur principale limite est que la dynamique neuronale capturée par l’EEG présente un faible rapport signal/bruit et une forte variabilité entre sujets, sessions et configurations d’enregistrement, rendant difficile l’apprentissage de modèles statistiques stables. Les défis des ICM-EEG ont mené au développement de nombreuses méthodes d’apprentissage profond dédiées. Pourtant, malgré ses capacités de transfert, l’apprentissage profond peine à s’imposer en ICM, faute de bases théoriques claires et d’évaluations rigoureuses. Cette thèse explore si les techniques existantes des principaux domaines de l’apprentissage profond peuvent être adaptées au décodage EEG plutôt que de concevoir de nouvelles méthodes spécifiques aux ICM. Nos résultats montrent comment les avancées en apprentissage profond peuvent améliorer les ICM en les rendant plus efficaces, avec une calibration réduite et une meilleure généralisation aux configurations d’électrodes et aux tâches
Apprentissage profond pour les applications interface cerveau-machine : exploiter les avancées récentes en vision par ordinateur
No full textBCI systems enable direct communication between brain activity and external devices, with applications in assistive technology, neurorehabilitation, and human-computer interaction. Despite their potential, EEG-based BCI remain mostly confined to laboratory prototypes. Their main limitation is that neuronal dynamics captured by EEG exhibit low signal-to-noise ratio and strong variability across subjects, sessions, recording settings, and tasks, making stable statistical modeling challenging. These difficulties have led to numerous EEG-BCI-specific deep learning methods. While deep learning excels in transfer learning, its impact on BCI remains limited due to the lack of clear theoretical foundations and rigorous evaluations. This dissertation explores whether existing techniques from leading deep learning fields can be adapted to EEG decoding instead of designing new BCIspecific methods. Our findings show how deep learning advances can enhance EEG-based BCIs, improving adaptability, efficiency, and generalization across electrode configurations and tasks, while reducing calibration time.Les ICM permettent une communication directe entre l’activité cérébrale et des dispositifs externes, avec des applications en technologie d’assistance, neuro-rééducation et interaction homme-machine. Malgré leur potentiel,les ICM basées sur l’EEG restent peu utilisées en pratique, la plupart étant confinées aux laboratoires. Leur principale limite est que la dynamique neuronale capturée par l’EEG présente un faible rapport signal/bruit et une forte variabilité entre sujets, sessions et configurations d’enregistrement, rendant difficile l’apprentissage de modèles statistiques stables. Les défis des ICM-EEG ont mené au développement de nombreuses méthodes d’apprentissage profond dédiées. Pourtant, malgré ses capacités de transfert, l’apprentissage profond peine à s’imposer en ICM, faute de bases théoriques claires et d’évaluations rigoureuses. Cette thèse explore si les techniques existantes des principaux domaines de l’apprentissage profond peuvent être adaptées au décodage EEG plutôt que de concevoir de nouvelles méthodes spécifiques aux ICM. Nos résultats montrent comment les avancées en apprentissage profond peuvent améliorer les ICM en les rendant plus efficaces, avec une calibration réduite et une meilleure généralisation aux configurations d’électrodes et aux tâches
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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