Method for generating decision implication canonical basis based on true premises

Li, Deyu; Zhang, Shaoxia; Zhai, Yongjie

doi:10.1007/s13042-016-0575-2

Cited by 12 publications

(18 citation statements)

References 26 publications

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“…Steps 10-20 remove the invalid decision premises and the candidate new decision premises that are not new decision premises (steps [17][18][19]. Note that, because the decision implications in O are stored in the increasing cardinality of decision premises (step 6) and O is traversed in order (step 10), we can compute Θ(A) (steps [11][12][13][14][15][16].…”

Section: For Allmentioning

confidence: 99%

Incremental Method of Generating Decision Implication Canonical Basis

Zhang

Zhai

2021

Preprint

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Decision implication is an elementary representation of decision knowledge in formal concept analysis. Decision implication canonical basis (DICB), a set of decision implications with completeness and nonredundancy, is the most compact representation of decision implications. The method based on true premises (MBTP) for DICB generation is the most efficient one at present. In practical applications, however, data is always changing dynamically, and MBTP has to re-generate inefficiently the whole DICB. This paper proposes an incremental algorithm for DICB generation, which obtains a new DICB just by modifying and updating the existing one. Experimental results verify that when the samples in data are much more than condition attributes, which is actually a general case in practical applications, the incremental algorithm is significantly superior to MBTP. Furthermore, we conclude that, even for the data in which samples is less than condition attributes, when new samples are continually added into data, the incremental algorithm must be also more efficient than MBTP, because the incremental algorithm just needs to modify the existing DICB, which is only a part of work of MBTP.

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Section: For Allmentioning

confidence: 99%

Incremental Method of Generating Decision Implication Canonical Basis

Zhang

Zhai

2021

Preprint

Self Cite

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“…It detects conceptual structures in data and consequently extraction of dependencies within the data by forming a collection of objects and their properties [10,11]. FCA is able to visualize and represent knowledge by exploring the relationship between objects and is known to be effective for data analysis and association rule extraction [12,13].…”

Section: Fca On Gri Indicatorsmentioning

confidence: 99%

Sustainability Assessment of Manufacturing Organizations Based on Indicator Sets: A Formal Concept Analysis

Eslami

Dassisti

Panetto

et al. 2019

On the Move to Meaningful Internet Systems: OTM 2018 Workshops

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Organizations are struggling to survive in today's competitive market. They are mostly obliged to meet customers' expectations and demand for sustainable products from one side and comply with governmental rules and regulations regarding energy, resources, materials, etc. on the other side. Therefore, measuring their sustainability performance and trying to keep up with the competitors is essential for their future development. Consequently, organizations' perception of operational sustainability can reveal their strategies on how to be sustainable, endeavouring the three pillars of economic, environmental and social. The present work investigates the role of indicators' choice and their meaning for sustainability assessment of manufacturing organizations. To this point, an analysis is conducted on the sustainability assessment of 100 manufacturing organizations using GRI indicators for assessing their sustainability state. A Formal Concept Analysis was run to look over the indicators and their interpretations to reach a given degree of sustainability of the organization.

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“…Decision‐making is of importance in all science‐based professions, where specialists apply their knowledge to make valuable decisions. This kind of knowledge can be captured by a decision implication

A false\Rightarrow B

, a term in formal concept analysis [1–5], expressing that when all conditions in A occur, one should take the decisions in B [6–10].…”

Section: Introductionmentioning

confidence: 99%

“…Similar to attribute implications [1, 11], the logical study of decision implications can be divided into two parts [7], the semantical aspect and the syntactical aspect. The semantical aspect accounts for the following questions on decision implications: The soundness of decision implications: how to determine whether a decision implication is valid ? Redundancy of decision implications: does there exists a decision implication that can be deduced from the other decision implications? Completeness of decision implications: how to obtain a compact set of valid decision implications from the given set of decision implications without loss of information? Decision implication basis [8–10]: how to derive a non‐redundant complete set of decision implications? In the syntactical aspect, one starts with a set of decision implications and some inference rules [6, 12], and then deduce new decision implications from the given set by repeatedly applying some inference rules. This process brings forth the following questions concerning the semantical aspect: The soundness of inference rules: is any deduced decision implication valid, provided that any decision implication taken from the given set is valid? Completeness of inference rules: when the given set is complete, can one obtain all valid decision implications only by repeatedly applying some inference rules? Redundancy of inference rules: can one obtain one inference rule from the others? Considering fuzzy values also existing in real datasets, Zhai et al extended decision implication to fuzzy decision implication and presented its semantical and syntactical aspects [13].…”

Section: Introductionmentioning

confidence: 99%