A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application

Kaburlasos, Vassilis G.; Pachidis, Theodore

doi:10.1016/j.inffus.2011.04.003

Cited by 34 publications

(22 citation statements)

References 78 publications

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“…Comparative computational experiments regarding image pattern recognition have demonstrated the viability of the proposed techniques. The good generalizability of the proposed classification schemes was attributed to the synergy of INs, which can represent all order data statistics [14], with image feature descriptors.…”

Section: Discussionmentioning

confidence: 99%

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Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Kaburlasos

Papakostas

2015

IEEE Comput. Intell. Mag.

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This paper describes the recognition of image patterns based on novel representation learning techniques by considering higher-level (meta-)representations of numerical data in a mathematical lattice. In particular, the interest here focuses on lattices of (Type-1) Inter vals' Numbers (INs), where an IN represents a distribution of image features including orthogonal moments. A neural classif ier, namely fuzzy lattice reasoning (f lr) fuzzy-ART-MAP (FAM), or f lrFAM for short, is described for learning distributions of INs; hence, Type-2 INs emerge. Four benchmark image pattern recognition applications are demonstrated. The results obtained by the proposed techniques compare well with the results obtained by alternative methods from the literature. Furthermore, due to the isomorphism between the lattice of INs and the lattice of fuzzy numbers, the proposed techniques are straightforward applicable to Type-1 and/or Type-2 fuzzy systems. The far-reaching potential for deep learning in big data applications is also discussed.

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Section: Discussionmentioning

confidence: 99%

“…Specific examples of the LC approach are described in [13]. Recent trends in LC appear in [5], [14], [16], [27].…”

Section: Introductionmentioning

confidence: 99%

Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Kaburlasos

Papakostas

2015

IEEE Comput. Intell. Mag.

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“…The empty set ( ) is also considered to be an interval [a 1 ,a 2 ] with any a 1 ,a 2 L such that a 1 a 2 . In particular, the is a positive valuation on lattice (L L, ) [10]. Hence, the function v (.)…”

Section: Generalmentioning

confidence: 99%

Induction of formal concepts by lattice computing techniques for tunable classification

Kaburlasos¹,

Moussiades²

2014

JESTR

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This work proposes an enhancement of Formal Concept Analysis (FCA) by Lattice Computing (LC) techniques. More specifically, a novel Galois connection is introduced toward defining tunable metric distances as well as tunable inclusion measure functions between formal concepts induced from hybrid (i.e., nominal and numerical) data. An induction of formal concepts is pursued here by a novel extension of the Karnaugh map, or K-map for short, technique from digital electronics. In conclusion, granular classification can be pursued. The capacity of a classifier based on formal concepts is demonstrated here with promising results. The formal concepts are interpreted as descriptive decisionmaking knowledge (rules) induced from the training data.

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“…Any employment of an inclusion measure function for decision-making is called Fuzzy Lattice Reasoning (FLR) [7]. Below, the interest focuses on complete lattices.…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

“…Future work will consider extensions to Intervals' Numbers (INs) [7]. Promising future applications include human activity recognition based on series of digital images as well as planning/scheduling under uncertainty.…”

Section: Definition 22 a Mass Functionmentioning

confidence: 99%

Fuzzy Lattice Reasoning (FLR) Extensions to Lattice-Valued Logic

Kaburlasos¹

2012

2012 16th Panhellenic Conference on Informatics

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This work introduces the Boolean (quotient) lattice ) , ( ⊆ , an element of whom is the union of countable (closed) intervals on the real line. It follows that ) , , , ( ' ∩ ∪ is a lattice implication algebra (LIA), the latter is an established framework for reasoning under uncertainty. It is illustrated in ) , , , ( ' ∩ ∪ how fuzzy lattice reasoning (FLR) techniques, for tunable decision-making, can be extended to lattice-valued logic. Potential practical applications are described. (Abstract)

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A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application

Cited by 34 publications

References 78 publications

Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

Induction of formal concepts by lattice computing techniques for tunable classification

Fuzzy Lattice Reasoning (FLR) Extensions to Lattice-Valued Logic

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