Fuzzy Class Membership Research Articles

Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to provide transparency, fairness, accuracy, generality, and comprehensibility to the results obtained from AI and ML algorithms in decision-making systems. The black box nature of AI and ML systems has remained a challenge in healthcare, and interpretable AI and ML techniques can potentially address this issue. Here we critically review previous studies related to the interpretability of ML and AI methods in medical systems. Descriptions of various types of XAI methods such as layer-wise relevance propagation (LRP), Uniform Manifold Approximation and Projection (UMAP), Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), ANCHOR, contextual importance and utility (CIU), Training calibration-based explainers (TraCE), Gradient-weighted Class Activation Mapping (Grad-CAM), t-distributed Stochastic Neighbor Embedding (t-SNE), NeuroXAI, Explainable Cumulative Fuzzy Class Membership Criterion (X-CFCMC) along with the diseases which can be explained through these methods are provided throughout the paper. The paper also discusses how AI and ML technologies can transform healthcare services. The usability and reliability of the presented methods are summarized, including studies on the usability and reliability of XGBoost for mediastinal cysts and tumors, a 3D brain tumor segmentation network, and the TraCE method for medical image analysis. Overall, this paper aims to contribute to the growing field of XAI in healthcare and provide insights for researchers, practitioners, and decision-makers in the healthcare industry. Finally, we discuss the performance of XAI methods applied in medical health care systems. It is also needed to mention that a brief implemented method is provided in the methodology section.

Efficient image retrieval system for textural images using fuzzy class membership

Efficient image retrieval system for textural images using fuzzy class membership

Content-Based Remote Sensing Image Retrieval Based on Fuzzy Rules and a Fuzzy Distance

The methods in remote sensing image retrieval (RSIR) usually search the whole retrieval data set in the retrieval process, which takes much time and is unnecessary. To reduce the overall search time, this letter proposes a new retrieval scheme based on fuzzy rules. The proposed method calculates the fuzzy class membership of images using two ways. The first way predicts the fuzzy class membership by convolutional neural network (CNN). The other uses the image-to-class distance that is a distance between an image and each class on the training data set. The two fuzzy class memberships are used to measure the classification confidence, and a query image is classified into three fuzzy sets, namely, “low classification confidence,” “medium classification confidence,” and “high classification confidence,” based on the classification confidence. The fuzzy rules are built according to fuzzy classification to choose the search space for each fuzzy set. The final search space is determined by the two search spaces obtained by fuzzy rules. Moreover, the fuzzy distance between a query image and a retrieved image is used to improve the retrieval performance, which is calculated according to their fuzzy class memberships and the Euclidean distance between the two images. The experimental results on University of California, Merced data set (UCMD) and PatternNet databases show that our proposed method can not only enhance the retrieval performance but also reduce the search time in comparison to other state-of-the-art techniques.

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

AbstractAn estimated 76% of global stream area is occupied by channels with widths above 30 m. Sentinel‐2 imagery with resolutions of 10 m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub‐pixel composition could further be used to compensate for small channel widths imaged at 10 m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land‐cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel‐2 imagery in order to develop a fuzzy classification approach capable of large‐scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high‐resolution class information that can be used to train fuzzy classification models for Sentinel‐2 data where all bands are super‐resolved to a spatial resolution of 10 m. We use a multi‐temporal UAV dataset covering an area of 5.25 km2. Using a novel convolutional neural network (CNN) classifier, we predict sub‐pixel membership for Sentinel‐2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from −5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection

Intrusion detection systems (IDS) can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols. To deal with the dynamic and complex nature of cyber-attacks, advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning (ML) and deep learning (DL). Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection capacity. However, just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security experts. Many papers have explored various dimensionality reduction algorithms, however a large number of selected features is still required to detect the attacks successfully, which humans cannot intuitively or immediately understand. To enhance user’s trust and understanding on data without sacrificing on accuracy, this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk indicator. To achieve this, a novel feature extraction pipeline was implemented consisting of the following components: (i) a fuzzy allocation scheme that transforms raw data to fuzzy class memberships, (ii) a novel modality transformation mechanism for converting feature vectors to images (Vec2im) and (iii) a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature space. The performance of the proposed methodology was validated with respect to detection accuracy, dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness (86.64% testing accuracy using only one feature) over a number of well-known feature selection (FS) and extraction techniques. The output of the proposed feature extraction pipeline could be potentially used by security experts as an indicator of malicious activity, whereas the generated images could be further utilized and/or integrated as a visual analytics tool in existing IDS.

Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images.

Pathologists are responsible for cancer type diagnoses from histopathological cancer tissues. However, it is known that microscopic examination is tedious and time-consuming. In recent years, a long list of machine learning approaches to image classification and whole-slide segmentation has been developed to support pathologists. Although many showed exceptional performances, the majority of them are not able to rationalize their decisions. In this study, we developed an explainable classifier to support decision making for medical diagnoses. The proposed model does not provide an explanation about the causality between the input and the decisions, but offers a human-friendly explanation about the plausibility of the decision. Cumulative Fuzzy Class Membership Criterion (CFCMC) explains its decisions in three ways: through a semantical explanation about the possibilities of misclassification, showing the training sample responsible for a certain prediction and showing training samples from conflicting classes. In this paper, we explain about the mathematical structure of the classifier, which is not designed to be used as a fully automated diagnosis tool but as a support system for medical experts. We also report on the accuracy of the classifier against real world histopathological data for colorectal cancer. We also tested the acceptability of the system through clinical trials by 14 pathologists. We show that the proposed classifier is comparable to state of the art neural networks in accuracy, but more importantly it is more acceptable to be used by human experts as a diagnosis tool in the medical domain.

Semantically Explainable Fuzzy Classifier

In machine learning, there are many high-performance classifiers. However, because of lack of transparency, they are not able to explain the data in a human-friendly form. In this paper, Cumulative Fuzzy Class Membership Criterion (CFCMC), a recently proposed fuzzy modeling classifier, is modified and utilized for a novel approach of information extraction from the labeled data. This approach is able to explain the classifiability of the data in the form of semantics. Extracted semantics give information about the structure of the data and the similarities between classes. To get a relevant image of its classification performance, it is compared to three well-known and frequently used classifiers, which are considered as black boxes, namely, SVM, MLP, and kNN, and to a similar transparent approach, MF ARTMAP. To validate extracted semantics, they are compared to visualization of classified data and to confusion matrices generated during the evaluation of the created CFCMC models. The experimental result shows that CFCMC is not necessarily the best classifier, although, in most cases, it is not too far from the best performing methods. However, the semantical explanation potentially allows the classifier to be applied as a support for human decision processes in real-world problems.

Evolutionary-modified fuzzy nearest-neighbor rule for pattern classification

Proposed a new fuzzy nearest neighbor rule with two new parameters.Proposed two new cost functions working in fuzzy class membership space.Verified the model reliability using experiments on several data sets.Statistically compared with several fuzzy based nearest neighbor rules.Implemented on graphical processing units for the sake of speed up. This paper presents an improved version of the well-established k nearest neighbor (k-NN) and fuzzy NN (FNN), termed the multi-objective genetic-algorithm-modified FNN (MOGA-MFNN). The MFNN design problem is converted into a multi-modal objective maximization problem constrained by four objective functions. Thereafter, the associated parameter set of the MFNN and the feature attributes can be determined optimally and automatically via the non-dominated sorting genetic algorithm II. We introduce two new objective functions termed the Margin-I and Margin-II, which are used to improve the generalization capability of the MFNN for the unknown data, along with two existing performance functions: the geometric mean and the area under the receiver-operated characteristic curve for the training accuracy. Moreover, we proposed a novel data-dependent weight-assignment technique for local class membership functions of the MFNN. The technique enables the MFNN to determine its local neighbors adaptively through the MOGA algorithm. To expedite the classification, the MOGA-MFNN is implemented on a graphical processing unit (GPU), which significantly increases the computation speed. Furthermore, the local class-membership function of the MFNN can be computed in advance, rather than delaying it to the classification stage. This again can improve the classification speed. The MOGA-MFNN is evaluated on 20 datasets obtained from the repository of the University of California, Irvine (UCI). The experiments with rigorous statistical significance tests demonstrate that the proposed method performs competitively with the existing methods.

Multi-functional nearest-neighbour classification

The k nearest-neighbour (kNN) algorithm has enjoyed much attention since its inception as an intuitive and effective classification method. Many further developments of kNN have been reported such as those integrated with fuzzy sets, rough sets, and evolutionary computation. In particular, the fuzzy and rough modifications of kNN have shown significant enhancement in performance. This paper presents another significant improvement, leading to a multi-functional nearest-neighbour (MFNN) approach which is conceptually simple to understand. It employs an aggregation of fuzzy similarity relations and class memberships in playing the critical role of decision qualifier to perform the task of classification. The new method offers important adaptivity in dealing with different classification problems by nearest-neighbour classifiers, due to the large and variable choice of available aggregation methods and similarity metrics. This flexibility allows the proposed approach to be implemented in a variety of forms. Both theoretical analysis and empirical evaluation demonstrate that conventional kNN and fuzzy nearest neighbour, as well as two recently developed fuzzy-rough nearest-neighbour algorithms can be considered as special cases of MFNN. Experimental results also confirm that the proposed approach works effectively and generally outperforms many state-of-the-art techniques.

Content‐based image retrieval using fuzzy class membership and rules based on classifier confidence

Content representation for images with well-defined inter-class boundaries in the feature space remains to be a difficult task. Simple distance-based retrieval (SDR) approaches those operate on the feature space for content-based image retrieval (CBIR) are, therefore claimed to be inefficient by many researchers. Different CBIR approaches have been proposed to surmount the drawbacks of SDR scheme. This study proposes a novel image retrieval scheme. In this scheme, effort is taken to reduce the overall search time of the recently proposed approach called ‘class membership-based retrieval’ (CMR). The proposed method identifies the confidence in the classification and limits the search to single output class and therefore, reduces the overall search time by 21.76% as compared to CMR. Quantitative methods are proposed to select various parameters used in the algorithm which were computed empirically in the case of earlier approach CMR. The computed parameters are validated using experimental results. The consistent behaviours of the proposed method and earlier methods used in the experiment are demonstrated using different feature sets and distance metrics. While the method can be used as a general purpose image retrieval system, experiment is performed on four texture databases wit different complexities in terms of size, number of texture classes and orientation.

Remembering Knowledge: An Expert Knowledge Based Approach to Digital Soil Mapping

A paradigm shift has occurred in the field of pedolody, moving away from traditional, tacit knowledge based soil survey methods to new, digital soil mapping (DSM) approaches. Although a variety of DSM techniques have been developed and are widely available, it is important not to lose sight of the wealth of existing soils information captured by historic soil survey and soil scientists. This paper presents a DSM method that combines expert knowledge with fuzzy logic and selective soil sampling to generate soil class and property maps for an approximately 16-ha (?40-acre) farm field in southeastern Indiana. Five soil classes were produced by implementing a fuzzy membership based approach utilizing four digital elevation model (DEM) derived terrain attributes: SAGA topographic wetness index (TWI), modified catchment area (MCA), slope percentage, and elevation. From the fuzzy class membership values, in combination with selective soil sampling and model calibration, calibrated and uncalibrated loess depth maps were generated. The mean absolute error (MAE) of predicted loess depth was 27 cm for uncalibrated prediction and 29 cm for calibrated prediction. Normalized yield data from 2011 found all five soil classes differed significantly in yield, giving evidence the five classes function differently in crop production. Peer Reviewed Papers

Content-based texture image retrieval using fuzzy class membership

There is no single best representation of images that can separate different classes with well defined boundaries in the feature space. Therefore, content-based image retrieval (CBIR) using conventional distance metric is not efficient in the low level image feature space viz. texture. Classifier based retrieval approaches (classification followed by retrieval) classify the query image and retrieve images only from the identified class. The performance of such approaches greatly relies on the performance of classifier. For each correct classification of query image, these systems yield high retrieval accuracy and for each misclassification the systems result in complete failure. It results huge variance in performance. This paper proposes a novel approach to content-based image retrieval called “Class Membership-based Retrieval” that addresses the limitations of both conventional distance based and conventional classifier based retrieval approaches. The proposed method consists of two steps. First, the class label and fuzzy class membership of query image is computed using neural network. In the second step, the retrieval is performed using a combination of simple and weighted (class membership based) distance metric in complete search space unlike the conventional classifier based retrieval techniques. The proposed technique also provides flexibility in reducing the search space in steps increasing the speed of retrieval at the cost of gradual reduction in accuracy. The performance of the method is evaluated using three texture data sets varying in orientations, complexity and number of classes. Experimental results support the proposed technique favorably when compared with other promising texture retrieval schemes.

A nonnegative sparse representation based fuzzy similar neighbor classifier

This paper presents a new classification technique by combining the well-known Sparse Representation based algorithm with the theory of Fuzzy Set. The basic idea of this work is that samples with the same class-labels should be more similar to each other than those with different class-labels. Based on this similarity rule, we first impose the nonnegative coefficient constraints on the sparse representation based algorithm and obtain the desirable similar neighbors. Then by introducing the theory of Fuzzy Set into our work, we construct the fuzzy class membership matrix and then assign the decision membership of the query sample to each class. The class assigned with the dominant decision membership is wanted. The proposed approach is called the Nonnegative Sparse Representation based Fuzzy Similar Neighbor Classification (FSNC). FSNC has the following properties: (a) the neighbor parameter K is not needed to be set in advance, and K is adaptively set by the algorithm itself; (b) similar neighbors are also generated adaptively and contain much more similar properties of the query sample; (c) the degree of similarity of data is clearly recorded in the sparse nonnegative coefficient vector; (d) the fuzzy decision rule is effective and the proposed classifier FSNC is simple. Experimental results conducted on the Wine database from UCI, the AR face database, the CENPARMI handwritten numeral database, and the PolyU palmprint database show that the new proposed classification technique outperforms some other state-of-the-art classifiers.

An Efficient Implementation of Weighted Fuzzy Fisherface Algorithm for Face Recognition Using Wavelet Transform

Problem statement: The paper addresses the face recognition problem by proposing Weighted Fuzzy Fisherface (WFF) technique using biorthogonal transformation. The weighted fuzzy fisherface technique was an extension of Fisher Face technique by introducing fuzzy class membership to each training sample in calculating the scatter matrices. Approach: In weighted fuzzy fisherface method, weight emphasizes classes that were close together and deemphasizes the classes that are far away from each other. Results: The proposed method was more advantageous for the classification task and its accuracy was improved. Also with the performance measures False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) were calculated. Conclusion: Weighted fuzzy fisherface algorithm using wavelet transform can effectively and efficiently used for face recognition and its accuracy is improved.

Fuzzy rough granular neural networks, fuzzy granules, and classification

We introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is described in terms of fuzzy granules, the target vector is defined in terms of fuzzy class membership values and zeros. Crude domain knowledge about the initial data is represented in the form of a decision table, which is divided into subtables corresponding to different classes. The data in each decision table is converted into granular form. The syntax of these decision tables automatically determines the appropriate number of hidden nodes, while the dependency factors from all the decision tables are used as initial weights. The dependency factor of each attribute and the average degree of the dependency factor of all the attributes with respect to decision classes are considered as initial connection weights between the nodes of the input layer and the hidden layer, and the hidden layer and the output layer, respectively. The effectiveness of the proposed FRGNN is demonstrated on several real-life data sets.

SCTWC: An online semi-supervised clustering approach to topical web crawlers

Focused web crawlers collect topic-related web pages from the Internet. Using Q learning and semi-supervised learning theories, this study proposes an online semi-supervised clustering approach for topical web crawlers (SCTWC) to select the most topic-related URL to crawl based on the scores of the URLs in the unvisited list. The scores are calculated based on the fuzzy class memberships and the Q values of the unlabelled URLs. Experimental results show that SCTWC increases the crawling performance.

Classification with incomplete survey data: a Hopfield neural network approach

Survey data are often incomplete. Classification with incomplete survey data is a new subject. This study proposes a Hopfield neural network based model of classification for incomplete survey data. Using this model, an incomplete pattern is translated into fuzzy patterns. These fuzzy patterns, along with patterns without missing values, are then used as the exemplar set for teaching the Hopfield neural network. The classifier also retains information of fuzzy class membership for each exemplar pattern. When presenting a test sample, the neural network would find an exemplar that best matches the test pattern and give the classification result. Compared with other classification techniques, the proposed method can utilize more information provided by the data with missing values, and reveal the risk of the classification result on the individual observation basis.

Evaluating Errors in a Digital Vegetation Map with Forest Inventory Data and Accuracy Assessment Using Fuzzy Sets

In large‐area mapping projects, existing reference data, often collected for a different purpose, are increasingly being used for map accuracy assessment. Multi‐attribute digital vegetation maps have been developed for all National Forest lands in California (8.1 million ha). We developed decision rules that could be applied to quantitative Forest Inventory and Analysis (FIA) plot data in order to score the fuzzy membership of plot locations in all possible map classes. We compare the accuracy of the vegetation map attributes estimated using this method to accuracy estimated from fuzzy class membership scores assigned by experts (inventory crews) during field work. Accuracy of the vegetation life form attribute was estimated to be higher when expert label assignments were used as reference data (76–87%), instead of FIA plot data (62–79%). This suggests that automated decision rules applied to detailed data from FIA plots, which have smaller area than map polygons, may systematically underestimate map accuracy. However, assignment of the actual map labels to FIA plot locations by inventory crews appears to be a robust method for using the FIA data for accuracy assessment.

Fuzzy class membership approach to soil erosion modelling

To optimize the use of the available land and water resources of a watershed for sustainable agricultural production, soil erosion assessment and conservation are essential. The Universal Soil Loss Equation (USLE) is a widely accepted model (Wishmeier and Smith, 1965. Predicting Rainfall Erosion Losses from Cropland (Agricultural Handbook No. 282). USDA. Washington, D.C.) for assessing soil erosion and to account for the most important factors. Conventional approaches to classification are designed to assign a given area element (pixel) to a single erosion class. However, the soil and other physical parameters might vary spatially within a pixel and it may not correspond entirely to a single erosion class. To determine the loss of information on the susceptibility to erosion a fuzzy class membership approach was used to assign partial grades to the erosion classes. In the present studies, a spatially distributed approach was used to consider the influence of the spatial variation in the soil and other physical parameters in soil erosion assessment in the study area, Kalyanakere sub-watershed Karnataka, India, using the USLE model. The emphasis of the studies was laid on the use of a fuzzy class membership approach in soil erosion classification, and to develop a criteria table specifying the erosion parameter values related to erosion susceptibility classes from available literature to apply fuzzy class membership approach for the classification. Salient features of the approach and the results of the study are presented in this paper.

The influence of fuzzy set theory on the areal extent of thematic map classes

In thematic maps information is traditionally represented in a onepolygon-one class method which assumes each polygon in the map can be assigned unambiguously to a single class. Additionally, thematic map classes are assumed to be exhaustive and mutually exclusive. The introduction of fuzzy classifications overcomes the traditional limitations on the mutually exclusive nature of map classes assigning varying levels of class membership for individual map polygons. As a consequence, fuzzy set theory expands the conventional assumptions regarding the total areal extent of thematic map classes. Considering that a map polygon may simultaneously belong to more than one thematic map class, the total areal extent of all classes in a thematic map need not be equal to the geographical extent of the study area. Although this apparent paradox has already been noted in previous papers, as far as we know,an analytical formulation of the influence of fuzzy classifications on the areal extent of thematic map classes has never been provided. This Letter offers an analytical solution to evaluate the total areal extent of thematic map classes as a function of fuzzy class membership levels.