77 research outputs found
LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification
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: Dr Jitender Deogu
Integrated intelligent systems for industrial automation: the challenges of Industry 4.0, information granulation and understanding agents .
The objective of the paper consists in considering the challenges of new automation paradigm Industry 4.0 and reviewing the-state-of-the-art in the field of its enabling information and communication technologies, including Cyberphysical Systems, Cloud Computing, Internet of Things and Big Data. Some ways of multi-dimensional, multi-faceted industrial Big Data representation and analysis are suggested. The fundamentals of Big Data processing with using Granular Computing techniques have been developed. The problem of constructing special cognitive tools to build artificial understanding agents for Integrated Intelligent Enterprises has been faced
LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION
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
Recommended from our members
Granular computing approach for intelligent classifier design
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the
underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different
classification systems to investigate its efficiency. Fuzzy inference systems, neural networks, neuro-fuzzy systems and classifier ensembles are considered to evaluate the efficiency of the proposed approach. Each of the considered systems is designed using the proposed approach and classification performance is evaluated and compared to that of the standard system. The proposed approach is based on constructing information granules from data at multiple levels of granularity. The granulation process is performed using a modified fuzzy c-means algorithm that takes classification problem into account. Clustering is followed by a coarsening process that involves merging small clusters into large ones to form a lower granularity level. The resulted granules are used to build each of the considered binary classifiers in different settings and approaches.
Granules produced by the proposed granulation method are used to build a fuzzy classifier for each granulation level or set of levels. The performance of the classifiers is evaluated using real life data sets and measured by two classification performance measures: accuracy and area under receiver operating characteristic curve. Experimental results show that fuzzy systems constructed using the proposed method achieved better classification performance. In addition, the proposed approach is used for the design of neural network classifiers. Resulted granules from one or more granulation levels are used to train the classifiers at different levels of specificity/abstraction. Using this approach, the classification problem is broken down into the modelling of classification rules represented by the information granules resulting in more interpretable system. Experimental results show that neural network classifiers trained using the proposed approach have better classification performance for most of the data sets. In a similar manner, the proposed approach is used for the training of neuro-fuzzy systems resulting in similar improvement in classification performance. Lastly, neural networks built using the proposed approach are used to construct a classifier ensemble. Information granules are used to generate and train the base classifiers. The final ensemble output is produced by a weighted sum combiner. Based on the experimental results, the proposed approach has improved the classification performance of the base classifiers for most of the data sets. Furthermore, a genetic algorithm is used to determine the combiner weights automatically.Higher Committee for Education Development in Iraq (HCED
Cognitive Models and Computational Approaches for improving Situation Awareness Systems
2016 - 2017The world of Internet of Things is pervaded by complex environments
with smart services available every time and everywhere. In
such a context, a serious open issue is the capability of information
systems to support adaptive and collaborative decision processes
in perceiving and elaborating huge amounts of data. This requires
the design and realization of novel socio-technical systems based on
the “human-in-the-loop” paradigm. The presence of both humans
and software in such systems demands for adequate levels of Situation
Awareness (SA). To achieve and maintain proper levels of
SA is a daunting task due to the intrinsic technical characteristics
of systems and the limitations of human cognitive mechanisms.
In the scientific literature, such issues hindering the SA formation
process are defined as SA demons.
The objective of this research is to contribute to the resolution
of the SA demons by means of the identification of information
processing paradigms for an original support to the SA and the
definition of new theoretical and practical approaches based on
cognitive models and computational techniques.
The research work starts with an in-depth analysis and some
preliminary verifications of methods, techniques, and systems of
SA. A major outcome of this analysis is that there is only a limited
use of the Granular Computing paradigm (GrC) in the SA
field, despite the fact that SA and GrC share many concepts and
principles. The research work continues with the definition of contributions
and original results for the resolution of significant SA
demons, exploiting some of the approaches identified in the analysis
phase (i.e., ontologies, data mining, and GrC). The first contribution addresses the issues related to the bad perception of data
by users. We propose a semantic approach for the quality-aware
sensor data management which uses a data imputation technique
based on association rule mining. The second contribution proposes
an original ontological approach to situation management,
namely the Adaptive Goal-driven Situation Management. The approach
uses the ontological modeling of goals and situations and
a mechanism that suggests the most relevant goals to the users at
a given moment. Lastly, the adoption of the GrC paradigm allows
the definition of a novel model for representing and reasoning
on situations based on a set theoretical framework. This model
has been instantiated using the rough sets theory. The proposed
approaches and models have been implemented in prototypical systems.
Their capabilities in improving SA in real applications have
been evaluated with typical methodologies used for SA systems. [edited by Author]XXX cicl
Eighth International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2020)
International audienceProceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020)co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 202
Attribute Exploration of Gene Regulatory Processes
This thesis aims at the logical analysis of discrete processes, in particular
of such generated by gene regulatory networks. States, transitions and
operators from temporal logics are expressed in the language of Formal Concept
Analysis. By the attribute exploration algorithm, an expert or a computer
program is enabled to validate a minimal and complete set of implications, e.g.
by comparison of predictions derived from literature with observed data. Here,
these rules represent temporal dependencies within gene regulatory networks
including coexpression of genes, reachability of states, invariants or possible
causal relationships. This new approach is embedded into the theory of
universal coalgebras, particularly automata, Kripke structures and Labelled
Transition Systems. A comparison with the temporal expressivity of Description
Logics is made. The main theoretical results concern the integration of
background knowledge into the successive exploration of the defined data
structures (formal contexts). Applying the method a Boolean network from
literature modelling sporulation of Bacillus subtilis is examined. Finally, we
developed an asynchronous Boolean network for extracellular matrix formation
and destruction in the context of rheumatoid arthritis.Comment: 111 pages, 9 figures, file size 2.1 MB, PhD thesis University of
Jena, Germany, Faculty of Mathematics and Computer Science, 2011. Online
available at http://www.db-thueringen.de/servlets/DocumentServlet?id=1960
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
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