290,959 research outputs found

    Concept learning consistency under three‑way decision paradigm

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    Concept Mining is one of the main challenges both in Cognitive Computing and in Machine Learning. The ongoing improvement of solutions to address this issue raises the need to analyze whether the consistency of the learning process is preserved. This paper addresses a particular problem, namely, how the concept mining capability changes under the reconsideration of the hypothesis class. The issue will be raised from the point of view of the so-called Three-Way Decision (3WD) paradigm. The paradigm provides a sound framework to reconsider decision-making processes, including those assisted by Machine Learning. Thus, the paper aims to analyze the influence of 3WD techniques in the Concept Learning Process itself. For this purpose, we introduce new versions of the Vapnik-Chervonenkis dimension. Likewise, to illustrate how the formal approach can be instantiated in a particular model, the case of concept learning in (Fuzzy) Formal Concept Analysis is considered.This work is supported by State Investigation Agency (Agencia Estatal de Investigación), project PID2019-109152GB-100/AEI/10.13039/501100011033. We acknowledge the reviewers for their suggestions and guidance on additional references that have enriched our paper. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

    Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

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    Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies

    FCA modelling for CPS interoperability optimization in Industry 4.0

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    International audienceCyber-Physical Systems (CPS) lead to the 4-th Industrial Revolution (Industry 4.0) that will have benefits of high flexibility of production, easy and so more accessible participation of all involved parties of business processes. The Industry 4.0 production paradigm is characterized by autonomous behaviour and intercommunicating properties of its production elements across all levels of manufacturing processes so one of the key concept in this domain will be the semantic interoperability of systems. This goal can benefit of formal methods well known various scientific domains like artificial intelligence, machine learning and algebra. So the current investigation is on the promising approach named Formal Concept Analysis (FCA) to structure the knowledge and to optimize the CPS interoperability

    Utilization of The Thrasher and Rice Mill Machines in Composition Function Learning: A Hypothetical Learning Trajectory Design

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    The study aims to design mathematics learning in composite function concepts with farm tools, which are thrasher and rice mill machines; this farm’s tool is used as to starting point in the learning process. The research method used is design research with a  preliminary design, design experiment, and analysis retrospective stages. This study describes the design of the thrasher and rice mill machine to facilitate a real contribution for student understanding of the composite function concept. The participant of this research is 10 eleventh-grade students from one of the senior high school in East Java. The results of this study reveal that students are able to make associations from the thrasher and rice mill machine through the determination of the input and output of the machines to the formula of the composite function concept. So, the stages in the learning trajectory have an important role in understanding the composition function concept from informal level to formal level and also make the study of mathematics more easy, simple, fun, and comfortable

    A Formal Proof of PAC Learnability for Decision Stumps

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    We present a formal proof in Lean of probably approximately correct (PAC) learnability of the concept class of decision stumps. This classic result in machine learning theory derives a bound on error probabilities for a simple type of classifier. Though such a proof appears simple on paper, analytic and measure-theoretic subtleties arise when carrying it out fully formally. Our proof is structured so as to separate reasoning about deterministic properties of a learning function from proofs of measurability and analysis of probabilities.Comment: 13 pages, appeared in Certified Programs and Proofs (CPP) 202

    LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION

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    Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Jitender Deogu
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