Davide Ciucci

Davide Ciucci
Università degli Studi di Milano-Bicocca | UNIMIB · Department of Informatics, Systems and Communication (DISCo)

PhD Computer Science
Reducing the Gap between Artificial Intelligence and Society (https://www.disco.unimib.it/en/node/654)

About

185
Publications
31,772
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Introduction
Davide Ciucci currently works at the Department of Informatics, Systems and Communication (DISCo), Università degli Studi di Milano-Bicocca. Davide does research in knowledge representation and uncertainty management
Additional affiliations
October 2017 - June 2023
Università degli Studi di Milano-Bicocca
Position
  • Professor (Associate)
February 2005 - September 2017
Università degli Studi di Milano-Bicocca
Position
  • Researcher
October 2011 - September 2012
Université Toulouse III - Paul Sabatier
Position
  • 3val - Three valued logics and uncertainty management
Description
  • Marie Curie Fellowship
Education
October 2000 - February 2004
University of Milan
Field of study
  • Computer Science
September 1994 - December 1999
University of Milan
Field of study
  • Computer Science

Publications

Publications (185)
Article
In recent years, Machine Learning (ML) has attracted wide interest as aid for decision makers in complex domains, such as medicine. Although domain experts are typically aware of the intrinsic uncertainty around it, the issue of Ground Truth (GT) quality has scarcely been addressed in the ML literature. GT quality is regularly assumed to be adequat...
Article
The aim of this article is to study the relationship between two popular Cautious Learning approaches, namely: Three-way decision (TWD) and conformal prediction (CP). Based on the novel proposal of a technique to transform three-way decision classifiers into conformal predictors, and vice-versa, we provide conditions for the equivalence between TWD...
Article
Rough set theory and belief function theory, two popular mathematical frameworks for uncertainty representation, have been widely applied in different settings and contexts. Despite different origins and mathematical foundations, the fundamental concepts of the two formalisms (i.e., approximations in rough set theory, belief and plausibility functi...
Article
Full-text available
In this article, we survey the applications of Three-way decision theory (TWD) in machine learning (ML), focusing in particular on four tasks: weakly supervised learning and multi-source data management, missing data management, uncertainty quantification in classification, and uncertainty quantification in clustering. For each of these four tasks...
Chapter
We introduce particular aggregation operators on shadowed sets, which derive from the operations between conditional events and from the consensus operator. Considering that shadowed sets arise as approximations of fuzzy sets, we also present and study special classes of aggregation functions that can be approximated by the considered operations on...
Chapter
In three-way decision theory, three disjoint sets covering a given universe, are determined: the positive, negative, and boundary regions. They correspond to three types of decisions on their objects: acceptance, rejection, and abstention or non-commitment. A linguistic approach for identifying the three regions relies on specific evaluative lingui...
Preprint
Full-text available
We propose a linguistic interpretation of three-way decisions, where the regions of acceptance, rejection, and non-commitment are constructed by using the so-called evaluative linguistic expressions, which are expressions of natural language such as small, medium, very short, quite roughly strong, extremely good, etc. Our results highlight new conn...
Chapter
In this article we introduce and describe scikit-weak, a Python library inspired by scikit-learn and developed to provide an easy-to-use framework for dealing with weakly supervised and imprecise data learning problems, which, despite their importance in real-world settings, cannot be easily managed by existing libraries. We provide a rationale for...
Chapter
Orthopartitions are partitions with uncertainty. We survey their use in knowledge representation (KR) and machine learning (ML). In particular, in KR their connection with possibility theory, intuitionistic fuzzy sets and credal partitions is discussed. As far as ML is concerned, their use in soft clustering evaluation and to define generalized dec...
Chapter
The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limi...
Chapter
This work highlights the connections between fuzzy orthopartitions and credal partitions, which are both mathematical structures. It is shown that fuzzy orthopartitions are a more general way to represent partitions with uncertainty than credal partitions.KeywordsFuzzy orthopartitionsCredal partitionsBayesian bbas
Article
In this work we present a large-scale comparison of 21 learning and aggregation methods proposed in the ensemble learning, social choice theory (SCT), information fusion and uncertainty management (IF-UM) and collective intelligence (CI) fields, based on a large collection of 40 benchmark datasets. The results of this comparison show that Bagging-b...
Article
Fuzzy orthopartitions are generalizations of standard fuzzy partitions useful to model situations where both vagueness and uncertainty are involved. This work deepens the study of fuzzy orthopartitions lately started in [1], focusing on their lower and upper entropies. The latter subsume the concept of entropy in partition logic and measures the qu...
Chapter
In this article, we study the problem of feature selection under weak supervision, focusing in particular on the fuzzy labels setting, where the weak supervision is provided in terms of possibility distributions over candidate labels. While traditional Rough Set-based approaches have been applied for tackling this problem, they have high computatio...
Preprint
The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limi...
Article
Orthopartitions are particular sets of orthopairs generalizing the notion of standard partitions in presence of uncertainty, and describing situations where the membership class of some elements of an initial universe is not precisely known. This work aims to bridge orthopartitions with possibility theory. Therefore, our principal contribution here...
Article
In this article, we study aggregation operators on shadowed sets. In particular, since shadowed sets can be obtained as approximations of fuzzy sets, we explore the relationships between aggregation operators on fuzzy sets and corresponding operators on shadowed sets. We focus on studying conditions under which the approximations of fuzzy sets into...
Preprint
Full-text available
This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare. In particular, we illustrate two main research themes related to the work of two laboratories within the Department of Informatics, Systems, and Communication at the University of Milano-Bicocca. The problems addressed conce...
Chapter
A hexagon of opposition built from a probabilistic rough set depends on two thresholds. This work explores the relations of opposition among vertices of hexagons obtained from pairs of thresholds. By an exhaustive analysis of the different cases that can arise, twelve patterns are defined and studied.KeywordsHexagon of oppositionProbabilistic rough...
Article
We present a novel kind of neighborhood, named subset neighborhood and denoted as Sρ-neighborhood. It is defined under an arbitrary binary relation using the inclusion relations between Nρ-neighborhoods. We study its relationships with some kinds of neighborhood systems given in the literature. Then, we formulate the concepts of Sρ-lower and Sρ-upp...
Article
In this article, we study the application of Rough Set theory to the representation of uncertainty and partial knowledge in Dynamical Systems. Our approach draws from the abstract notion of an observable pattern, and for this purpose we first propose an abstract knowledge representation formalism that encompasses the main classes of discrete Dynami...
Chapter
In this work, we bridge possibility theory with intuitionistic L-fuzzy sets, by identifying a special class of possibility distributions corresponding to intuitionistic \(\textsf {L}\)-fuzzy sets based on a complete residuated lattice with an involution. Moreover, taking the \(\L \)ukasiewicz n-chains as structures of truth degrees, we propose an a...
Chapter
In this article, we study the setting of learning from fuzzy labels, a generalization of supervised learning in which instances are assumed to be labeled with a fuzzy set, interpreted as an epistemic possibility distribution. We tackle the problem of feature selection in such task, in the context of rough set theory (RST). More specifically, we con...
Article
Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption is relaxed in the settings of weakly supervised learning, where labels are allowed to be imprecise or partial. In this article, we study the setting of superset learning, in which instan...
Article
In this paper, decision-theoretic five-way approximation of fuzzy sets is introduced by extending existing three-way decision-theoretic models. The proposed model exhibits a number of useful features which allow a decision maker to consider weak acceptance or weak rejection options, thereby minimizing the overall approximation error and cost. Two s...
Book
The volume LNAI 12872 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2021, Bratislava, Slovak Republic, in September 2021. The conference was held as a hybrid event due to the COVID-19 pandemic. The 13 full paper and 7 short papers presented were carefully reviewed and selected from 26 submissions, along with...
Article
We propose a method to approximate Intuitionistic Fuzzy Sets (IFSs) with Shadowed Sets that could be used, in decision making or similar tasks, when the full information about membership values is not necessary, is difficult to process or to interpret. Our approach is based on an information‐theoretic perspective and aims at preserving the uncertai...
Chapter
In this work, we study the theoretical properties, from the perspective of learning theory, of three-way clustering and related formalisms, such as rough clustering or interval-valued clustering. In particular, we generalize to this setting recent axiomatic characterization results that have been discussed for classical hard clustering. After propo...
Chapter
Ensemble learning provides a theoretically well-founded approach to address the bias-variance trade-off by combining many learners to obtain an aggregated model with reduced bias or variance. This same idea of extracting knowledge from the predictions or choices of individuals has been also studied under different perspectives in the domains of soc...
Article
Full-text available
Background: Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a gener...
Chapter
In this work we investigate how Rough Set Theory could be employed to model uncertainty and information incompleteness about a Reaction System. The approach that we propose is inspired by the idea of an abstract scientific experiment: we define the notion of test, which defines an approximation space on the states of a Reaction System, and observat...
Chapter
In this work we introduce a framework, based on three-way decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML). We distinguish between handling uncertainty affecting the input of ML models, when TWD is used to identify and properly take into account the uncertain instances; and handling the uncertai...
Chapter
Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption, which may constitute a severe bottleneck in the practical use of ML, is relaxed in weakly supervised learning. In this ML paradigm, training instances are not necessarily precisely labe...
Article
Full-text available
In clustering-based active learning, the performance of the learner relies heavily on the quality of clustering results. Empirical studies have shown that different clustering techniques are applicable to different data. In this paper, we propose the three-way active learning through clustering selection (TACS) algorithm to dynamically select the a...
Article
In this paper, we address ambiguity, intended as a characteristic of any data expression for which a unique meaning cannot be associated by the computational agent for either lack of information or multiple interpretations of the same configuration. In particular, we will propose and discuss ways in which a decision-support classifier can accept am...
Book
The volume LNAI 12179 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2020, which was due to be held in Havana, Cuba, in June 2020. The conference was held virtually due to the COVID-19 pandemic. The 37 full papers accepted were carefully reviewed and selected from 50 submissions. The papers are grouped in th...
Chapter
In this paper we focus on the importance of interpreting the quality of the input of predictive models (potentially a GI, i.e., a Garbage In) to make sense of the reliability of their output (potentially a GO, a Garbage Out) in support of human decision making, especially in critical domains, like medicine. To this aim, we propose a framework where...
Chapter
Machine learning–based decision support systems (DSS) are attracting the interest of the medical community. Their usage, however, could have deep consequences in terms of biasing the doctor’s interpretation of a case through automation bias and deskilling. In this work we address the design of DSS with the goal of minimizing these biases through th...
Chapter
Ambiguity, that is the lack of information to produce a specific classification, is an important issue in decision–making and supervised classification. In case of ambiguity, human–decision makers can resort to abstaining from making precise classifications (especially when error-related costs are high), but this behaviour has been scarcely address...
Article
In this work, we introduce the notion of orthopartition as a generalized partition with uncertainty. Several entropy-based measures are then developed to measure this intrinsic uncertainty, which are in turn applied to soft clustering. An application is explored: the use of the new Soft Mutual Information Measures to evaluate the performances of so...
Article
Belnap–Dunn four-valued logic is one of the best known logics for handling elementary information items coming from several sources. More recently, a conceptually simple framework, namely a two-tiered propositional logic augmented with classical modal axioms (here called BC logic), was suggested by the second author and colleagues, for the handling...
Conference Paper
Uncertainty is an intrinsic component of the clinical practice, which manifests itself in a variety of different forms. Despite the growing popularity of Machine Learning–based Decision Support Systems (ML-DSS) in the clinical domain, the effects of the uncertainty that is inherent in the medical data used to train and optimize these systems remain...
Chapter
Full-text available
This paper considers the use of machine learning in medicine by focusing on the main problem that it has been aimed at solving or at least minimizing: uncertainty. However, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of this class of computational mode...
Book
This LNAI 11499 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2019, held in Debrecen, Hungary, in June 2019. The 41 full papers were carefully reviewed and selected from 71 submissions. The IJCRS conferences aim at bringing together experts from universities and research centers as well as the industry repre...
Article
Full-text available
We show that finite IUML-algebras, which are residuated lattices arising from an idempotent uninorm, can be interpreted as algebras of sequences of orthopairs whose main operation is defined starting from the three-valued Sobociński operator between rough sets. Our main tool is the representation of finite IUML-algebras by means of finite forests....
Chapter
Clustering external indices are used to compare the clustering result with a given gold standard, represented (in the classical case) by a partition of the dataset. Rough clustering on the other hand splits the dataset in subsets with uncertain boundaries such that different clusters may overlap, i.e., the result is a covering instead of a partitio...
Article
In data mining, neighborhood classifiers are valid not only for numeric data but also symbolic data. The key issue for a neighborhood classifier is how to measure the similarity between two instances. In this paper, we compare six similarity measures, Overlap, Eskin, occurrence frequency (OF), inverse OF (IOF), Goodall3, and Goodall4, for symbolic...
Chapter
In this chapter we are interested to study the structures arising from pairs of elements from a partially ordered set (poset) which share some orthogonality between them, the so-called orthopairs, with respect to a unary operation of De Morgan complementation (or in the case of a lattice interpreted as De Morgan negation).
Book
This book constitutes the refereed proceedings of the 12th International Conference on Scalable Uncertainty Management, SUM 2018, which was held in Milan, Italy, in October 2018. The 23 full, 6 short papers and 2 tutorials presented in this volume were carefully reviewed and selected from 37 submissions. The conference is dedicated to the manageme...
Article
This paper introduces fuzzy sets and offers an overview of their theory after more than fifty years since their appearance, occurred in 1965 due to Lotfi Zadeh. Besides the basic notions, some hints on the historical context in which they developed and the disciplines they have contributed to are put forward. Then, the major successes are illustrat...
Article
Full-text available
In this paper we apply rough set theory to information tables induced from finite directed graphs without loops and multiples arcs (digraphs). Specifically, we use the adjacency matrix of a digraph as a particular type of information table. In this way, we are able to explore on digraphs the notions of indiscernibility partitions, lower and upper a...
Conference Paper
The departing point of this study is a data table with certainty values associated to attribute values. These values are deeply rooted in possibility theory, they can be obtained with standard procedures and they are efficiently manageable in databases. Our aim is to study rough set approximations and reducts in this framework. We define three cate...
Article
Full-text available
This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input o...
Conference Paper
In many situations information comes in bipolar form. Orthopairs are a simple tool to represent and study this kind of information, where objects are classified in three different classes: positive, negative and boundary. The scope of this work is to introduce some uncertainty measures on orthopairs. Two main cases are investigated: a single orthop...
Conference Paper
This paper proposes a conceptually simple but expressive framework for handling propositional information stemming from several sources, namely a two-tiered propositional logic augmented with classical modal axioms (BC-logic), a fragment of the non-normal modal logic EMN, whose semantics is expressed in terms of two-valued monotonic set-functions c...
Chapter
There are several notions and terms in Rough Set Theory not having a crystal clear definition. I discuss here two basic ones: Rough Set and Information System. The discussion will be lead by the two founding papers by Z. Pawlak. We will see that the term Information System has a narrow sense (the most used one in the rough set community) and a wide...
Article
In rough set theory (RST), and more generally in granular computing on information tables (GRC-IT), a central tool is the Pawlak’s indiscernibility relation between objects of a universe set with respect to a fixed attribute subset. Let us observe that Pawlak’s relation induces in a natural way an equivalence relation ≈ on the attribute power set t...
Book
This two-volume set LNAI 10313 and LNAI 10314 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2017, held in Olsztyn, Poland, in July 2017. The 74 revised full papers presented together with 16 short papers and 16 invited talks, were carefully reviewed and selected from 130 submissions. The papers in this two s...
Article
Full-text available
We introduce and study new generalizations of some rough set tools. Namely, the extended core, the generalized discernibility function, the discernibility space and the maximum partitioner. All these concepts where firstly introduced during the application of rough set theory to graphs, here we show that they have an interesting and useful interpre...
Article
Two methods are proposed for collective knowledge extraction from questionnaires with ordinal scales and dichotomous questions. Both methods are based on a three-way decision procedure and a statistical method aimed at attaining statistical significance of the above decision. One method is aimed at giving an (absolute) assessment of “objects” acco...
Conference Paper
In this paper we consider sequences of orthopairs given by refinement sequences of partitions of a finite universe. While operations among orthopairs can be fruitfully interpreted by connectives of three-valued logics, we describe operations among sequences of orthopairs by means of the logic IUML of idempotent uninorms having an involutive negatio...
Article
Full-text available
Pairs of disjoint sets (orthopairs) naturally arise or have points in common with many tools to manage uncertainty: rough sets, shadowed sets, version spaces, three-valued logics, etc. Indeed, they can be used to model partial knowledge, borderline cases, consensus, examples and counter-examples pairs. Moreover, generalized versions of orthopairs a...
Article
In this paper we study the lattice of all indiscernibility partitions induced from attribute subsets of a knowledge representation system (information table in the finite case). This lattice, that we here call granular partition lattice, is a very well studied order structure in granular computing and data base theory and it provides a complete hie...
Article
The paper shows that a cube of opposition, a structure that generalizes the square of opposition invented in ancient logic, can be generated from the composition of a binary relation with a subset, by the effect of set complementation on the subset, on the relation, or on the result of the composition. Since the composition of relations is encounte...
Article
Full-text available
We present a unique framework for connecting different topics: hypergraphs from one side and Formal Concept Analysis and Rough Set Theory from the other. This is done through the formal equivalence among Boolean information tables, formal contexts and hypergraphs. Links with generic (i.e., not Boolean) information tables are established, through so...
Article
The square of opposition is as old as logic. There has been a recent renewal of interest on this topic, due to the emergence of new structures (hexagonal and cubic) extending the square. They apply to a large variety of representation frameworks, all based on the notions of sets and relations. After a reminder about the structures of opposition, an...
Conference Paper
Full-text available
The incidence matrix of a simple undirected graph is used as an information table. Then, rough set notions are applied to it: approximations, membership function, positive region and discernibility matrix. The particular cases of complete and bipartite graphs are analyzed. The symmetry induced in graphs by the indiscernibility relation is studied a...
Chapter
Full-text available
The adjacency relation of a simple undirected graph is a preclusive (irreflexive and symmetric) relation. Hence, it originates a preclusive space enabling us to define the lower and upper preclusive approximations of graphs and two orthogonality graphs. Further, the possibility of defining the similarity lower and upper approximations and the suffi...
Research
Full-text available
Proc. RSFDGrC 2015, Lecture Notes in Computer Science, Vol. 9437, Springer 2015 (To appear).
Research
Full-text available
Proc. RSFDGrC 2015, Lecture Notes in Computer Science, Vol. 9437, Springer 2015 (To appear).
Research
Full-text available
Proc. RSFDGrC 2015, Lecture Notes in Computer Science, Vol. 9437, Springer 2015 (To appear).
Research
Full-text available
Proc. RSKT 2015, Lecture Notes in Computer Science, Vol. 9436, Springer 2015 (To appear).
Research
Full-text available
Proc. RSKT 2015, Lecture Notes in Computer Science, Vol. 9436, Springer 2015 (To appear).
Article
Considerable progress has been made in the theory of covering-based rough sets. However, there has been a lack of research on their application to classification tasks, especially for nominal data. In this paper, we propose a representative-based classification approach for nominal data using covering-based rough sets. The classifier training task...
Conference Paper
Full-text available
The adjacency matrix of a graph is interpreted as a formal context. Then, the counterpart of Formal Concept Analysis (FCA) tools are introduced in graph theory. Moreover, a formal context is seen as a Boolean information table, the structure at the basis of Rough Set Theory (RST). Hence, we also apply RST tools to graphs. The peculiarity of the gra...
Article
Full-text available
The adjacency matrix of a graph is interpreted as a formal context. Then, the counterpart of Formal Concept Analysis (FCA) tools are introduced in graph theory. Moreover, a formal context is seen as a Boolean information table, the structure at the basis of Rough Set Theory (RST). Hence, we also apply RST tools to graphs. The peculiarity of the gra...
Chapter
Full-text available
The significance of three-valued logics partly depends on the interpretation of the third truth-value. When it refers to the idea of unknown, we have shown that a number of three-valued logics, especially Kleene, Łukasiewicz and Nelson's can be encoded in a simple fragment of the modal logic KD, called MEL, containing only modal formulas without ne...
Chapter
This chapter reviews three formulations of rough set theory, i.e., element-based rough sets, granule-based rough sets, and subsystem-based rough sets. These formulations are adopted to generalize rough sets from three directions. They are formulated by using an arbitrary binary relation to generalize the equivalence relation in the element-based de...
Book
This book constitutes the thoroughly refereed conference proceedings of the 10th International Conference on Rough Sets and Knowledge Technology, RSKT 2015, held in Tianjin, China, in November 2015, as part of the International Joint Conference on Rough Sets, IJCRS 2015, together with the 15th International Conference on Rough Sets, Fuzzy Sets, Dat...
Article
In this paper we compare the expressive power of elementary representation formats for vague, incomplete or conflicting information. These include Boolean valuation pairs introduced by Lawry and Gonzalez-Rodriguez, orthopairs of sets of variables, Boolean possibility and necessity measures, three-valued valuations, supervaluations. We make explicit...

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