Rough Set Theory

Raza, Muhammad Summair; Qamar, Usman

doi:10.1007/978-981-10-4965-1_3

Cited by 2 publications

(4 citation statements)

References 38 publications

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“…It is calculated by determining the presence of such attribute in each reduct set. Conditional attributes that are part of the core are thus indispensable from a modelling standpoint 9.

true Core = \cap_{i = 1}^{n} R_{i}

…”

Section: Methodsmentioning

confidence: 99%

“…This indiscernibility relation is the mathematical basis of rough set theory. In RSML, such relationships allow approximate models consisting of if-then decision rules to be generated from data [9]. Rough set theory also provides rigorous basis for removing redundant variables from models, and provides performance metrics to quantify their predictive and generalization capability [8].…”

Section: Rough Set-based Machine Learningmentioning

confidence: 99%

“…These contributions showed that RSML can deal with both numerical and non-numerical conditional attributes. Overall experience with RSML shows its capability to handle real world datasets [9]. Furthermore, the if-then rules that comprise RSML models lend themselves to easy interpretation in many applications.…”

Section: Rough Set-based Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

A Fragrance Prediction Model for Molecules Using Rough Set‐based Machine Learning

Tiew

Chew

Lee

et al. 2022

Chemie Ingenieur Technik

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In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set-based machine learning is used to generate rule-based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules.

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“…It is calculated by determining the presence of such attribute in each reduct set. Conditional attributes that are part of the core are thus indispensable from a modelling standpoint 9.

true Core = \cap_{i = 1}^{n} R_{i}

…”

Section: Methodsmentioning

confidence: 99%

Section: Rough Set-based Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Fragrance Prediction Model for Molecules Using Rough Set‐based Machine Learning

Tiew

Chew

Lee

et al. 2022

Chemie Ingenieur Technik

View full text Add to dashboard Cite

show abstract

“…An information system is defined by a pair (U, A), where U is the finite nonempty set of objects (universe) and A is the objects 'attributes. For every attribute a ∈ A, it has a value set defined by a value, V a as shown in Equations ( 1) and (2) [39].…”

Section: Rough Set-based Machine Learning (Rsml)mentioning

confidence: 99%

Incorporating Machine Learning in Computer-Aided Molecular Design for Fragrance Molecules

et al. 2022

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The demand for new novel flavour and fragrance (F&F) molecules has boosted the need for a systematic approach to designing fragrance molecules. However, the F&F-related industry still relies heavily on experimental approaches or on existing databases without considering the consequences resulting from changes in concentration, which could omit potential fragrances. Computer-aided molecular design (CAMD) has great potential to identify novel molecular structures to be used as fragrances. Using CAMD for this purpose requires models to predict the olfaction properties of molecules. A rough set-based machine learning (RSML) approach is used to develop an interpretable predictive model for odour characteristics in this work. New rule-based models are generated from RSML based on the dilution and a number of different topological indices which identify the structure-odour relationship of fragrance molecules. The most prominent rules are selected and formulated as constraints in a CAMD optimisation model. The combination of several rules was able to increase the coverage of different classes of molecules. To model the performance indicators that vary over a range of properties, a disjunctive programming model is also incorporated into the CAMD framework. A case study demonstrates the utilisation of this methodology to design fragrance additives in dishwashing liquid. The results illustrate the capability of the novel RSML and CAMD framework to identify potential fragrance molecules that can be used in consumer products.

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Rough Set Theory

Cited by 2 publications

References 38 publications

A Fragrance Prediction Model for Molecules Using Rough Set‐based Machine Learning

A Fragrance Prediction Model for Molecules Using Rough Set‐based Machine Learning

Incorporating Machine Learning in Computer-Aided Molecular Design for Fragrance Molecules

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