A method to build similarity relations into extended Rough Set Theory

Filiberto, Yaima; Caballero, Yailé; Larrúa, Rafael; Bello, Rafael

doi:10.1109/isda.2010.5687091

Cited by 24 publications

(14 citation statements)

References 24 publications

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“…A different weight was assigned to each attribute. It was realized manually and using the Particle Swarm Optimization (PSO) method, implemented in the PROCONS software (PSO+RST (Rougt Set Theoryc) (15) The variable selected to predict was the oxygen radical absorbance capacity (ORAC exp ) value, expressed in µmol TE/100 g. ORAC was selected because it is considered to be the preferable methodology to evaluate the antioxidant capacity due to its biological relevance to the in vivo antioxidant efficacy (16). ORAC exp and TP exp (mg GAE/100 g) for each substrate were found in the literature.…”

Section: Procedures For the Prediction Using Artificial Intelligence Amentioning

confidence: 99%

<strong>Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods</strong>

Yordi

Koeling

Mota

et al. 2015

Proceedings of the 19th International Electronic Conference on Synthetic Organic Chemistry

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Flavonoids are bioactive compounds that can display antioxidant activity. Their must important source is the vegetal kingdom. Their composition in different foods is compiled into several databases organized by USDA. This information enabled the creation of a data record that was used in the work to predict the total antioxidant capacity of food by the oxygen radical absorbance capacity (ORAC) method, using algorithms of artificial intelligence. K-Nearest Neighbors (KNN) was used. The attributes were: a) amount of flavonoid, b) class of flavonoid, c) Trolox equivalent antioxidant capacity (TEAC) value, d) probability of clastogenicity and clastogenicity classification by Quantitative Structure-Activity Relationship (QSAR) method and e) total polyphenol (TP) value. The selected variable to predict was the ORAC value. For the prediction, a cross-validation method was used. For the KNN algorithm, the optimal K value was 3, making clear the importance of the similarity between objects for the success of the results. It was concluded the successful use of the KNN algorithm to predict the antioxidant capacity in the studied food groups.

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Section: Procedures For the Prediction Using Artificial Intelligence Amentioning

confidence: 99%

<strong>Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods</strong>

Yordi

Koeling

Mota

et al. 2015

Proceedings of the 19th International Electronic Conference on Synthetic Organic Chemistry

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show abstract

“…However, in the study presented in [14,34], the initialization of the MLP with a single hidden layer is considered. The values for the weights of the connections between the input nodes and the nodes in the hidden layer are initialized using the weights of features according to the maxQS method described in section 3.…”

Section: Application In Mlpmentioning

confidence: 99%

“…The new problem is finding the appropriate similarity relation for each application domain. A method to build similarity relations based on the similarity quality measure is proposed in [13,14], this includes to compute the weights for the features.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, it is analyzed the solution proposed in [13,14] to build similarity relations. The similarity relation is formulated as xRy ⇔ F (x, y) ≥ ε, x, y ∈ U , and F (x, y) is a similarity function with values in [0,1].…”

Section: Introductionmentioning

confidence: 99%

“…This is an optimization problem that can be solved using a heuristic search, in which the heuristic evaluation function defined by the similarity quality measure is maximized. In [13,14], the Particle Swarm Optimization (PSO) [28] has been used to solve the problem. In this paper (to see section 4.1) an extension of the study is presented by using as heuristic method the Univariant Marginals Distribution Algorimth (UMDA), instead of PSO.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An analysis about the measure quality of similarity and its applications in machine learning

Filiberto¹,

Bello²,

Caballero³

et al. 2013

Procedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support

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In this paper, a review about the quality of the similarity measure and its applications in machine learning is presented. This measure is analyzed from the perspective of the granular computing. The granular computing allows analyzing the information at different levels of abstraction and from different approaches. The analysis shows that this measure is based on two basic aspects on the universe of objects: the granularity of the information and the principle that, similar problems have similar solutions. Using the measure, a method was formulated to build relations of similarity; these relations and other results have been used in improving machine learning techniques.

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Methods for Generating Contexts Based on Similarity Relations to Multigranulation

Arias

Filiberto

Bello

2018

Communications in Computer and Information Science

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A method to build similarity relations into extended Rough Set Theory

Cited by 24 publications

References 24 publications

<strong>Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods</strong>

<strong>Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods</strong>

An analysis about the measure quality of similarity and its applications in machine learning

Methods for Generating Contexts Based on Similarity Relations to Multigranulation

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