A Study on the Approximation of Clustered Data to Parameterized Family of Fuzzy Membership Functions for the Induction of Fuzzy Decision Trees

Narayanan, Swathi Jamjala; Paramasivam, Ilango; Bhatt, Rajen B.; Khalid, Maryam

doi:10.1515/cait-2015-0030

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…2 tq is the input data partition region width; u m tk is the k th sample; x k belongs to the t th membership value; m is the fuzzy degree introduced by the fuzzy clustering algorithm; q k is category value [12]. After AFCM clustering, the membership function of the corresponding characteristic parameter x k to R t is (x k ), then (x k ) can be expressed as follows [13]:…”

Section: Derivation Of Membership Function By Using Equipment Performmentioning

confidence: 99%

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Deng

Yao

Lu³

2018

Mathematical Problems in Engineering

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Flexible photoelectric film is an anisotropic material. The slight change of equipment performance during processing is prone to cause deformation of the material. Therefore, it is important to predict the degradation of processing equipment performance. Since the performance degradation of flexible photoelectric film material Roll-to-Roll (R2R) processing equipment is a nonlinear process, this paper introduces an adaptive fuzzy clustering method to construct a fuzzy membership function model for calculating the performance degradation index of R2R processing equipment and studies the parameter solving method such as the AFCM division of the roller vibration data, the category center value of the fuzzy membership function, and the input data division area width. Finally, the performance degradation index calculation algorithm is designed. The roller shaft accelerated life test was carried out using self-made equipment. The test data were 1000 sets. The results showed that the root mean square eigenvalues and the kurtosis eigenvalues of the roller vibration data are sensitive to the performance degradation. The equipment performance curve described by the first and second types of performance degradation indicators was very stable in the early stage. After the 800th group, the curve continued to decrease, and the change was more severe, indicating that the performance degradation of the equipment is more serious. In the 980th group, the longer-lasting roller shaft was damaged, and the performance index value was about zero, which proved the correctness of the performance degradation prediction method proposed in this paper in calculating the performance degradation value of the equipment.

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Section: Derivation Of Membership Function By Using Equipment Performmentioning

confidence: 99%

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Deng

Yao

Lu³

2018

Mathematical Problems in Engineering

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“…A new variant of firefly colony named fuzzy firefly colony approach is used to handle the uncertainties that could exist when fireflies makes a choice to choose next virtual machine for placement in current server. In this variant, the control strategies of the fireflies are established using fuzzy rules [41][42][43][44][45][46][47]. The fuzzy firefly colony algorithm follows same placement procedure given in Algorithm 1 except that step 17 in the procedure is modified to include fuzzy strategy and fuzzy probable strategy for choosing the next virtual machine for placement.…”

Section: Fuzzy Firefly Colony Approach For Server Consolidationmentioning

confidence: 99%

A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Perumal

Murugaiyan²

2016

Advances in Fuzzy Systems

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Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy.

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“…Liu et al (2013) built an AFS decision tree based on AFS theory and optimized the structure of the decision tree using a genetic algorithm. Narayanan et al (2015) used three fuzzy partitioning techniques, namely fuzzy C-means clustering, grid partitioning and subtractive clustering, to induce fuzzy decision trees. Wang et al (2015b) proposed a multi-label decision tree algorithm based on fuzzy rough sets, which it could tackle with symbolic, continuous and fuzzy data.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, based on the Map-Reduce programming model for generating both binary and multi-way trees from large data sets, a distributed fuzzy decision tree learning scheme was proposed by Segatori et al (2017). From the works of Bujnowski et al (2015), Chandra and Varghese (2008), Liu et al (2013), Manwani and Sastry (2012), Narayanan et al (2015, 2016), Segatori et al (2017), Umano et al (1994) and Wang et al (2000, 2015b), we know that the fuzzy decision trees can handle uncertainty and are easily accountable, but they cannot deal with the classification problem if the classification boundary is not parallel to the axis.…”

Section: Introductionmentioning

confidence: 99%

“…From the discussions of fuzzy decision trees (Bujnowski et al, 2015; Chandra and Varghese, 2008; Liu et al, 2013; Manwani and Sastry, 2012; Narayanan et al, 2015, 2016; Segatori et al, 2017; Umano et al, 1994;Wang et al, 2000, 2015b), oblique decision trees (Barros et al, 2014; Breiman et al, 1984; Cantu-Paz and Kamath, 2003; Kretowski, 2004; Li et al, 2003; Manwani and Sastry, 2012; Rivera-Lopez and Canul-Reich, 2017; Shah and Sastry, 1999; Wickramarachchi et al, 2016) and fuzzy rules decision trees (FRDTs) (Wang et al, 2015c), a FODT is proposed in this paper; its growth structure is depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New classification technique: fuzzy oblique decision tree

Cai

Sun

2018

Transactions of the Institute of Measurement and Control

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Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold δ to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.

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A Study on the Approximation of Clustered Data to Parameterized Family of Fuzzy Membership Functions for the Induction of Fuzzy Decision Trees

Abstract: This paper investigates the Triangular, Trapezoidal and Gaussian approximation methods for the purpose of induction of Fuzzy Decision Trees (FDT).

Cited by 13 publications

References 25 publications

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

New classification technique: fuzzy oblique decision tree

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