Fuzzy Principal Component Analysis Research Articles

Production planning and control of a pharmaceutical supply chain using evolutionary fuzzy approach

This study aims to enhance the effectiveness of planning a pharmaceutical or healthcare supply chain (PHSC). Past research mostly applied simulation or optimization methods, which were time-consuming or difficult to obtain the optimal solution. This study proposes an evolutionary fuzzy approach from a multiple-criteria decision-making (MCDM) perspective to addresses this issue. Primary factors considered in the proposed methodology include product quality, capacity, availability, experience and relationship, and product cost. In the proposed methodology, a calibrated fuzzy inverse of column sum (cFICSM) method is devised to derive the fuzzy priorities of criteria. Fuzzy principal component analysis (FPCA) is proposed to resolve the dependencies among criteria. Fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) is then applied to evaluate and compare PHSC partners. Finally, the required pieces are distributed among PHSC partners. The proposed methodology has been applied to plan a PHSC for medical masks in central Taiwan during the COVID-19 pandemic. Based on the experimental results, the proposed approach minimizes the makespan while increasing the compliance with customers’ preferences by 29 %. This study concludes that planning a PHSC from an MCDM perspective can achieve synergistic effects.

Application of Fuzzy algorithms in conjunction with 1 H-NMR spectroscopy for alcoholic beverages differentiation.

According to recent statistics elaborated at the European Union level, wine represents one of the most falsified commodities. In this context, the development of reliable classification models for alcoholic beverages differentiation requires, besides sensitive analytical tools, the use of the most suitable data processing methods like those based on advanced statistical tools or artificial intelligence. The present study aims to establish a new innovative approach for alcoholic beverages differentiation (wines and fruit distillates) able to increase the discrimination rate of the developed models. Thus, a data dimensionality reduction step, performed on the obtained 1 H-NMR profiles, was applied. This stage consisted in the application of Fuzzy Principal Component Analysis (FPCA) before themodel development through discriminant analysis. The enhancement of the classification potential given by the application of FPCA as compared to Principal Component Analysis (PCA) was discussed. The association between 1 H-NMR spectroscopy and an appropriate statistical approach proved to be a very effective tool for alcoholic beverages differentiation. In order to develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1 H-NMR spectroscopic data were exploited in corroboration with fuzzy algorithms for data dimensionality reduction. The study proved the efficiency of using FPCA scores over those obtained through the widely applied PCA. Based on the proposed approach, it was proved that for wine classification a perfect separation was obtained according to the geographical origin, cultivar and vintage. A percentage of 100% correctly classified samples was also achieved for botanical and geographical differentiation of fruit distillates. This article is protected by copyright. All rights reserved.

Determination of Vital Cancer Sites in Malaysian Colorectal Cancer Dataset by Using A Fuzzy Feature Selection Method

In Malaysia, Colorectal Cancer (CRC) is one of the most common cancers that occur in both men and women. Early detection is very crucial and it can significantly increase the rate of survival for the patients and if left untreated can lead to death. With the lack of high-quality CRC data, expert systems and machine learning analysis are burdened with the presence of irrelevant features, outliers, and noise. This can reduce the classification accuracy for data analysis. Accordingly, it is essential to find a reliable feature selection method that can identify and remove any irrelevant feature while being resistant to noise and outliers. In this paper, Fuzzy Principal Component Analysis (FPCA) was tested for the classification of Malaysian’s CRC dataset. With the utilization of fuzzy membership in FPCA, the experimental results showed that the proposed method produces higher accuracy compared to PCA and SVM by almost 2% and 5% respectively. Empirical results showed that FPCA is a reliable feature selection method that can find the most informative features in the CRC dataset that could assist medical practitioners in making an informed decision.

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

Fault Detection and Health Assessment of Equipment Based on Fuzzy DPCA Spatial Eigenvalue Similarity

To improve the fault recognition rate of the dynamic principal component spatial data drive method, a fault diagnosis and equipment health status assessment method based on similarity fuzzy dynamics principal component analysis was proposed. First, the data are fuzzified according to the error function, and an augmented matrix is constructed. The eigenvalues are decomposed to obtain a score matrix and residual matrix of the fuzzy principal component. Further, the similarity between fault data and normal data is calculated. Meanwhile, a health assessment of the equipment is realized. The contribution rate of the observed variables is calculated. Finally, general Tennessee Eastman data and health assessment of a hydraulic press are used to validate the algorithm. The results show that the SFDPCA has a 100 % fault recognition rate for some faults, and the recognition rate for other faults is also higher than that of DPCA-Diss, DPCA-SPE, and PCA-SPE. The SDDPCA accurately identifies abnormal phenomena. It can determine the health level of prefilling and effectively make up for the shortcomings of PCA − T 2 , PCA-SPE, DPCA-Diss, and other methods and also can be applied to data-driven fault diagnosis to improve the fault recognition rate.

Fuzzy Principal Component Analysis Model on Evaluating Innovation Service Capability

This article mainly evaluates the regional innovation service capacity through the TOPSIS method. Firstly, a regional collaborative innovation network is constructed and the Yangtze River Delta region is selected for analysis. Secondly, an evaluation index is constructed for innovation service capability, fuzzy principal component analysis is used to refine quantitative and qualitative index data of innovation service capability, and the index weight is calculated. Then the region of the Yangtze River Delta is selected and TOPSIS method is used to assist in the effective decision-making process of the evaluation of innovative service capabilities. Due to the large amount of data in this article, MATLAB programming is used. Finally, through the comparative analysis of the results, countermeasures and suggestions are put forward from the perspective of the improvement of collaborative innovation service capabilities.

Recognition of Bangla Handwritten Number Using Combination of PCA and FIS with the Aid of DWT

The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: “one and nine” and “five and six”. We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.

Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: a new chemometric approach.

A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associative-clustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).

Resting state fMRI analysis using unsupervised learning algorithms

ABSTRACTRS-fMRI data analysis for functional connectivity explorations is a challenging topic in computational neuroimaging. Several approaches have been investigated to discover whole-brain data features. Among these, clustering techniques based on Competitive Learning (CL) and Spectral Methods (SM) have been shown effective in providing useful information in various contexts. We selected three clustering algorithms and two spectral methods, i.e the clustering algorithm are Self-organising Maps (SOM), Neural Gas (NG) and Growing Neural Gas (GNG), whereas the spectral methods are the classic Principal Component Analysis (PCA) and the Nonlinear Robust Fuzzy Principal Component Analysis (NRFPCA). We validated clustering with Davies–Bouldin Index (DBI) and we selected informative principal components using Random Matrix Theory (RMT). tools. We adopted these techniques to study the intrinsic functional properties of images coming from a shared repository of resting state fMRI experiments (1000 Functional Connectome Project).

A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development

Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants.

Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. Principal component analysis (PCA) is a widely used method for dimensionality reduction. However, it is well-known that PCA and most PCA-based feature extraction methods are sensitive to noise, which may affect the accuracy of the subsequent classification. To address this problem, here we have proposed a robust fuzzy principal component analysis (PCA) with interval type-2 (IT-2) fuzzy membership functions for feature extraction. We have tested the performance of three widely used classifiers using the features extracted by proposed approaches and other feature extraction methods - PCA-based feature extraction methods (i.e. conventional PCA and fuzzy PCA), linear discriminant analysis (LDA), and support vector machine recursive feature elimination (SVM-RFE). The proposed feature extraction approaches showed better performance on cancer transcriptome and proteome datasets.

Missing data imputation with fuzzy feature selection for diabetes dataset

Missing data in datasets remain as a difficulty in terms of data analysis in various research fields, especially in the medical field, as it affects the treatment and diagnosis that the patient should receive. In this research, Fuzzy c-means (FCM) are used to impute the missing data. However, like in most data imputation methods, FCM do not consider the presence of irrelevant features. Irrelevant features can increase the computational time of the imputation process and decrease the accuracy of the prediction. Feature selection techniques can alleviate this problem by selecting the most relevant features and reducing the dataset size. Fuzzy principal component analysis (FPCA) is used as the feature selection method in this study as it considers the presence of outliers compared to classical PCA as outliers are the main reason some features renders irrelevant. Therefore, an improved hybrid imputation model of FPCA–Support vector machines–FCM (FPCA–SVM–FCM) has been proposed and employed in this study. The efficiency of the proposed model is investigated on one dataset which is Pima Indians Diabetes dataset. Experimental results showed that the proposed hybrid imputation model is better than the existing methods by producing a more accurate estimation in terms of accuracy, RMSE and MAE. The proposed method was also validated by using Wilcoxon rank sum and Theil’s U test and obtained good results compared to SVM–FCM. Therefore, it can be used as an alternative tool for handling missing data in order to obtain a better quality dataset.

A comprehensive classification of edible oils according to their radical scavenging spectral profile evaluated by advanced chemometrics

A comprehensive study concerning the characterization and classification of 30 cold-pressed edible oils according to their UV-Vis spectra and radical scavenging profiles using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay is presented. Considering the principal component analysis (PCA) and fuzzy-principal component analysis (FPCA) loadings profiles, the characteristic spectral regions with a significant influence in oil samples classification were identified and associated with characteristic factors in each group. Much more, the oils with high antiradical capacity were revealed. The scores corresponding to the first principal component and the canonical scores corresponding to the first discriminant function derived from radical scavenging spectral profiles allowed a relevant classification of oils in well-defined groups associated with their high, medium and low radical scavenging capacity. The FPCA-LDA method applied on DPPH radical scavenging spectral profiles of edible oils appeared to be the most efficient method with a correct classification rate of 96.7%.

基于模糊数学和主成分分析的数据融合算法研究

基于模糊数学和主成分分析的数据融合算法研究

Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics

Thin layer chromatography in combination with image analysis and advanced chemometric methods were successfully used to classify the medicinal herbs according to their therapeutic effects and usage. The investigations were conducted using two types of plates (HPTLC Silica gel 60 and HPTLC Silica gel 60 F254) which were evaluated in UV light at 254 and 365 nm. The holistic evaluation of the numerical data corresponding different image processing channels (blue, grey, red, green) was performed by employing appropriate multivariate methods: hierarchical cluster analysis (HCA), principal component analysis (PCA), fuzzy principal component analysis (FPCA) and linear discriminant analysis (LDA) applied to the first relevant principal components. The results obtained by applying LDA method indicate a highly accurate separation of the medicinal herbs within the four groups, in good agreement with therapeutic effects and usage. According to this classification, the best image processing channels were identified for each of the investigated HPTLC plates: blue channel for HPTLC Silica gel 60 F254 (with 92.9% percent of discrimination in case of PCA and FPCA) and respectively red channel for HPTLC Silica gel 60 (with 93.9% percent of discrimination in case of FPCA). The 2D and 3D score scatterplots illustrate also the accurate and reliable discrimination between the four distinct groups.

Application of robust face recognition in video surveillance systems

In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

Characterization of Clinically Relevant Fungi via SERS Fingerprinting Assisted by Novel Chemometric Models.

Nonculture-based tests are gaining popularity and upsurge in the diagnosis of invasive fungal infections (IFI) fostered by their main asset, the reduced analysis time, which enables a more rapid diagnosis. In this project, three different clinical isolates of relevant filamentous fungal species were discriminated by using a rapid (less than 5 min) and sensitive surface-enhanced Raman scattering (SERS)-based detection method, assisted by chemometrics. The holistic evaluation of the SERS spectra was performed by employing appropriate chemometric tools-classical and fuzzy principal component analysis (FPCA) in combination with linear discriminant analysis (LDA) applied to the first relevant principal components. The efficiency of the proposed robust algorithm is illustrated on the data set including three fungal isolates (Aspergillus fumigatus sensu stricto, cryptic A. fumigatus complex species, and Rhizomucor pusillus) that were isolated from patient materials. The accurate and reliable discrimination between species of common fungal pathogen strains suggest that the developed method has the potential as an alternative, spectroscopic-based routine analysis tool in IFI diagnosis.

Robust Principal Component Analysis based on Fuzzy Coded Data

In the presence of outlier s in the dataset, the principal component analysis method, like many of the classical statistical methods, is severely affected. For this reason, if there are outliers in dataset, researchers tend to use alternative methods. Use of fuzzy and robust approaches is the leading choice among these methods. In this study, a new approach to robust fuzzy principal component analysis is proposed. This approach combines the power of both robust and fuzzy methods at the same time and collects these two approaches under the framework of principal component analysis. The performance of proposed approach called robust principal component analysis based on fuzzy coded data is examined through a set of artificial dataset that are generated by considering three different scenarios and a real dataset to observe how it is affected by the increase in sample size and changes in the rate of outliers. In light of the study's findings, it is seen that the proposed approach gives better results than the ones in the classical and robust principal component analysis in the presence of outliers in dataset.

Discrimination of haloarchaeal genera using Raman spectroscopy and robust methods for multivariate data analysis

In this study, we aimed at developing a rapid and non‐destructive method to predict the genus‐level taxonomic affiliation of phenotypically similar halophilic archaea of Halobacteria Class. For this purpose, representatives of three widespread and frequently isolated haloarchaeal genera (Halobacterium sp., Haloferax sp. and Halorubrum sp.) were investigated by means of Raman spectroscopy. The most relevant Raman bands exhibited at 1505, 1150 and 1000 cm−1, respectively, were ascribed to the bacterioruberin carotenoid found in haloarchaea. Due to the similar spectral profiles, a robust chemometric approach based on fuzzy principal component analysis and linear discriminant analysis for the classification of the three halophilic strains was employed, and the high‐accuracy grouping of spectral data corresponding to those strains was achieved. Copyright © 2017 John Wiley & Sons, Ltd.

Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches

The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na+, K+, Ca2+, Mg2+, HCO3−, Cl−, F−, SO42−, PO43−, NO3−), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F− species and the anthropogenic one for NO3−, SO42−, PO43− and K+ and (ii) classification of groundwater based on content of arsenic species. The HCO3− type of local groundwater and alkaline pH (8.31–8.49) were found to be responsible for the enrichment of arsenic species and occurrence of F− but by different paths. The PO43-–AsO43- ion exchange, water-rock interaction (silicates hydrolysis and desorption from clay) were associated to arsenate enrichment in the oxidizing aquifer. Fuzzy Hierarchical Cross-Clustering was the strongest tool for the rapid simultaneous classification of groundwaters as a function of arsenic content and hydrogeochemical characteristics. The approach indicated the Na+–F-–pH cluster as marker for groundwater with naturally elevated As and highlighted which parameters need to be monitored. A chemical conceptual model illustrating the natural and anthropogenic paths and enrichment of As and co-occurring species in the local groundwater supported by mineralogical analysis of rocks was established.