Fuzzy-Based Techniques in Human-Like Processing of Social Network Data

Zadeh, Lotfi A.; Abbasov, Ali M.; Shahbazova, Shahnaz N.

doi:10.1142/s0218488515400012

Cited by 24 publications

(6 citation statements)

References 32 publications

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“…Necessity of the use of fuzzy inference system [ 48 ] is determined by the possible contradiction of the different classifier results for individual samples. To solve this problem, we propose to form the final solution using fuzzy inference system.…”

Section: Materials and Methodsmentioning

confidence: 99%

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Babichev

Škvor

2020

Diagnostics

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In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients’ health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient’s health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients’ gene expression profiles—the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps—in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers—logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient’s health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

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Section: Materials and Methodsmentioning

confidence: 99%

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Babichev

Škvor

2020

Diagnostics

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“…Necessity of the use of fuzzy inference system [48] is determined by the possible contradiction of the different classifiers results for individual samples. To solve this problem, we propose to form the final solution using fuzzy inference system.…”

Section: Fuzzy Inference System Implementationmentioning

confidence: 99%

Technique of Gene Expression Profiles Extraction based on the Complex Use of Clustering and Classification Methods

Babichev¹,

Škvor²

2020

Preprint

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In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ensemble of binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles: the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps: in beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using SOTA clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters were these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was selection of the optimal cluster at each of the hierarchical levels which corresponded to minimum value of the quality criterion. At the next step, we have implemented classification procedure of the examined objects using four well known binary classifiers: logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC analysis using criteria included as the components the errors of both the first and the second kinds. The final decision concerning extraction of the most informative subset of gene expression profiles was taken based on the use of fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

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“…Golsefid et al [59] proposed a fuzzy clustering model for detecting overlapping communities in complex networks. Their proposed model was developed based on the CPM clustering model [60] and assigns each node to each cluster by degree of belonging over an interval [0,1]. Therefore, instead of one node belonging to exactly one cluster, it can belong to more than one cluster, and associated with each node was a set of membership levels.…”

Section: Fl-based Topological Structure Miningmentioning

confidence: 99%

“…All authors read and approved the final manuscript. 1 4 Center for Radio Administration & Technology Development, Xihua University, 610039 Chengdu, China.…”

mentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

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Fuzzy-Based Techniques in Human-Like Processing of Social Network Data

Cited by 24 publications

References 32 publications

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Technique of Gene Expression Profiles Extraction based on the Complex Use of Clustering and Classification Methods

When social computing meets soft computing: opportunities and insights

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