Fuzzy K-nearest Neighbor Classifier Research Articles

Local means-based fuzzy k-nearest neighbor classifier with Minkowski distance and relevance-complementarity feature weighting

This paper introduces an enhanced fuzzy k-nearest neighbor (FKNN) approach called the feature-weighted Minkowski distance and local means-based fuzzy k-nearest neighbor (FWM-LMFKNN). This method improves classification accuracy by incorporating feature weights, Minkowski distance, and class representative local mean vectors. The feature weighting process is developed based on feature relevance and complementarity. We improve the distance calculations between instances by utilizing feature information-based weighting and Minkowski distance, resulting in a more precise set of nearest neighbors. Furthermore, the FWM-LMFKNN classifier considers the local structure of class subsets by using local mean vectors instead of individual neighbors, which improves its classification performance. Empirical results using twenty different real-world data sets demonstrate that the proposed method achieves statistically significantly higher classification performance than traditional KNN, FKNN, and six other related state-of-the-art methods.

Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback

Gastric cancer (GC), an acknowledged malignant neoplasm, threatens life and digestive system functionality if not detected and addressed promptly in its nascent stages. The indispensability of early detection for GC to augment treatment efficacy and survival prospects forms the crux of this investigation. Our study introduces an innovative wrapper-based feature selection methodology, referred to as bCIFMVO-FKNN-FS, which integrates a crossover-information feedback multi-verse optimizer (CIFMVO) with the fuzzy k-nearest neighbors (FKNN) classifier. The primary goal of this initiative is to develop an advanced screening model designed to accelerate the identification of patients with early-stage GC. Initially, the capability of CIFMVO is validated through its application to the IEEE CEC benchmark functions, during which its optimization efficiency is measured against eleven cutting-edge algorithms across various dimensionalities—10, 30, 50, and 100. Subsequent application of the bCIFMVO-FKNN-FS model to the clinical data of 1632 individuals from Wenzhou Central Hospital—diagnosed with either early-stage GC or chronic gastritis—demonstrates the model's formidable predictive accuracy (83.395%) and sensitivity (87.538%). Concurrently, this investigation delineates age, gender, serum gastrin-17, serum pepsinogen I, and the serum pepsinogen I to serum pepsinogen II ratio as parameters significantly associated with early-stage GC. These insights not only validate the efficacy of our proposed model in the early screening of GC but also contribute substantively to the corpus of knowledge facilitating early diagnosis.

Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer

The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics—achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.

BSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes. This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO. To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets. The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection

The EEG is an indispensable tool for clinical diagnosis of neurological disorders. The issue that how to correctly decoding the pathological information from EEG has been attracting a lot of interest. This paper creates a robust feature extraction scheme based on synchrosqueezing short-time Fourier transform (SSTFT) and graph regularized non-negative matrix factorization (GNMF), which is applied to epileptic EEG detection. The SSTFT is employed to analyze the EEG in time–frequency domain, followed by a GNMF decomposition that maps these components into a low-dimensional representation where latent clues are easily emphasized and learned, and finally the logarithmic energy, computed as features, is fed to a fuzzy K-Nearest Neighbor classifier for the purpose of discriminating seizure events. Our method is evaluated using two public databases including Bonn database and CHB-MIT database. Obtained results confirm the superiority of the proposed features by achieving classification accuracy of at least 98.0% for both short-term and long-term EEG recordings. It is expected to offer a new thought and perspective for the study and design of the automatic EEG monitoring system.

Fuzzy parameterized fuzzy soft k-nearest neighbor classifier

In this paper, we propose a new kNN algorithm, i.e., Fuzzy Parameterized Fuzzy Soft kNN (FPFS-kNN), based on multiple pseudo-metrics of fuzzy parameterized fuzzy soft matrices (fpfs-matrices). FPFS-kNN can consider the impacts of parameters on classification using pseudo-metrics of fpfs-matrices – a new concept. Furthermore, FPFS-kNN detects the nearest neighbors for each pseudo-metric and classifies data applying the aforementioned multiple distance functions. To demonstrate the classification success of the proposed method, we carry out an experimental study using 35 UCI datasets and comparing it with the state-of-the-art kNN-based and non-kNN-based algorithms. All the methods are trained and tested for ten runs through five-fold cross-validation. We then compare the results of FPFS-kNN with those of the others in terms of the most frequently used measures, such as accuracy (ACC), precision (PRE), recall (REC), micro F-score (MICF), and macro F-score (MACF). Afterward, we pro-vide a statistical evaluation of the results. Experimental and statistical results manifest that the proposed FPFS-kNN, utilized Pearson’s correlation coefficient and denoted by FPFS-kNN(P), outperforms the state-of-the-art kNN-based algorithms in 24 of 35 datasets in terms of each considered measure and 31 of 35 datasets in terms of accuracy measure. Besides, the results showed that FPFS-kNN(P) performs better than the others for 29 datasets in terms of ACC and MICF rates, and 24 datasets in terms of PRE, REC and MACF rates. Finally, we discuss FPFS-kNN for further research.

Evolutionary self-organizing fuzzy system using fuzzy-classification-based social learning particle swarm optimization

A self-organizing algorithm based on an online cluster and fuzzy sets update algorithm (OCFU) and fuzzy-classification-based social learning particle swarm optimization (FC-SLPSO) is proposed to address the problem of rule initialization and fitness function evaluation. The OCFU algorithm is used to determine the structure of the fuzzy system and build a flexible partition of fuzzy sets in each input variable. The FC-SLPSO algorithm establishes a fuzzy K-Nearest Neighbor (KNN) classifier in each iteration. The fitness evaluation is performed only when the membership degree of the offspring particles belonging to potential particles is greater than that of the parent particles, which effectively reduces the number of fitness function evaluations and accelerates the PSO algorithm. The tracking results of three nonlinear systems show that the tracking accuracy of the proposed method is better. The Pioneer P3DX mobile robot model and simulation environment with a two-dimensional lidar are constructed based on the Coppeliasim robot simulation platform. Using the algorithms proposed in this paper, the tracking task of the pioneer p3dx mobile robot along a wall is achieved. The effectiveness of the proposed algorithm is verified by the cosimulation of Coppeliasim and MATLAB in multiple scenarios.

An Intelligent RPL attack detection using Machine Learning-Based Intrusion Detection System for Internet of Things

The Internet of Things (IoT) is an important and a major key component to support human life. Sensors are the sensing devices that play an important role in IoT to detect environmental conditions. The significance of IoT is increasing due to the increase in the things connected to the internet using Internet Protocol version-6 (IPv6). Low-Power and Lossy Network (LLN) Routers used in IoT environment have limited processing, memory, and energy resources and hence it can't use the traditional routing protocols such as Open Shortest Path First (OSPF), Routing Information Protocol (RIP), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing(DSR) etc. However, Routing Protocol for Low power and Lossy Networks (RPL) is one of the prominent protocols which are developed to overcome the above issues but it could be exposed to specific types of attacks such as decreased rank attack, black hole attack, sinkhole attack, selective forwarding attack and version attacks, etc. Hence, it is necessary to develop a mechanism to detect and prevent such attacks. In this paper, efficient machine learning based Intrusion Detection System for Internet of Things is proposed to monitor the network activities against attacks and to detect the intruders more efficiently. This proposed machine learning based detection model performs feature selection and classification based on two new algorithms proposed in this paper called genetic recursive feature selection algorithm and fuzzy k-nearest neighbor classifier and detects the aforementioned attacks with maximum accuracy and minimum false positive rate. This work is also investigated on other performance metrics like precision, recall and F1-score to show the performance comparison between this proposed work with genetic recursive feature selection and fuzzy k-nearest neighbor classification algorithms and the existing works on intrusion detection.

Adaptive thresholding technique based classification of red blood cell and sickle cell using Naïve Bayes Classifier and K-nearest neighbor classifier

Detection of anomalous cells by analyzing the microscopic blood smear plays a key role in identification of various blood diseases in medical field. It becomes easier with robust segmentation steps followed by a classifier for automatic recognition of normal as well as anomalous blood cells. Sickle cell anemia is a disease that changes shape of the red blood cell (RBC) into sickle cell which can be detected by use of automatic classifier. In this reported work, a novel robust hybrid segmentation method is introduced for segmenting normal RBCs and sickle cells. The hybrid segmentation process proposed here is a combination of two segmentation methods namely fuzzy C-means and adaptive (local) thresholding. Four different adaptive thresholding methods are experimented here in combination with fuzzy C-means. The features extracted for carrying out this analysis are major axis, minor axis, aspect ratio, area, perimeter, form factor (metric value), eccentricity and solidity of each of the cells present in the sample blood smear. These eight features are used for training and testing the classifiers. For classification, two supervised classifiers, i.e. Naïve Bayes classifier and K-nearest neighbor classifier have been demonstrated on a dataset of 10 image data samples and the evaluated results are compared for each of the hybrid combinations. The comparison shows that the proposed hybrid combination of fuzzy C-means and NICK's thresholding alongwith K-nearest neighbor classifier gives the best performance in terms of the considered performance parameters. This combination gives a significant classification accuracy of 98.87% with lowest computational time.

Digital inspection approach of overlapped peaks due to high counting rates in neutron spectroscopy

This paper is concerned with digital inspection of normalized neutron pulses in the presence of high counting rate. An approach including several digital signal processing algorithms is implemented for such purpose. Initially, block diagram programming through a Matlab environment is conducted for the generation of normalized neutron pulses that degraded with white Gaussian noise (WGN). In the second stage, identification and classification of overlapped neutron pulses are demonstrated. Features reduction is applied to the input neutron events using several methods. A fuzzy K-nearest neighbor (fuzzy-KNN) classifier is applied for matching of extracted features. The inspected peaks confirm that fuzzy-KNN achieves 99% accuracy with applied extracted features. Accuracy of fuzzy-KNN classifier is compared with artificial neural network (ANN). The ANN demonstrates 100% accuracy with feature reduction on expenses of computational time. In third stage, handling of identified overlapped neutron pulses due to high counting rates is carried out. Five algorithms are implemented. The proposed algorithms are sensitivity, scale-space peak picking (SSPP), threshold peak, robustness peak detection (RPD) and crest detection with undecimated discrete wavelet transform (UDWT). The threshold peak besides crest detection with UDWT algorithms introduced less accurate recovery. Finally, resolution enhancement of visualized spectrum with special emphasis on complex spectrum is demonstrated. Five algorithms are built for narrowing the normalized neutron spectrum. These algorithms are smoothing sharpening, power law, optimum sharpening factors, power converted area and segmented sharpen algorithms. Resolution enhancement of normalized neutron spectrum by the power law algorithm is the best observable sharpening form. The accomplished high accuracy of the proposed approach for neutron spectrometry provides higher confidential nuclear security within industrial applications.

Localised edge‐region‐based active contour for medical image segmentation

Segmenting the region of interest (ROI) from medical images is a fundamental but challenging task due to the illumination change and imaging devices. Although many models based on the local region-based active contour model (LR-ACM) are proposed to deal with intensity inhomogeneity, it is still difficult for the global energy-based ACM with local image information to accurately extract the ROI from medical images. To solve this problem, this study proposes a novel localised ACM by constructing the gradient information based on the probability scores from the fuzzy k-nearest neighbour classifier. Different from the traditional LR-ACMs, our model is based on local rather than global image statistics, where a probability score-based edge detector is directly used for gradient information. The energy functional consists of localised region energy and an edge energy. By introducing a local characteristics function, the localised region energy with the probability-score-based edge information is formulated, which can make the evolution curve stop on the exact boundaries of ROI. The edge energy including the regularisation and the penalty terms is used to avoid the reinitialisation process and smooth the evolution curve during evolution. To maintain the signed distance property of the evolution curve, a novel potential function in the penalty term is designed, which can consistently control the diffusion direction of the evolution curve. Experiments on the medical images including the cardiac magnetic resonance imaging and the 3DIRCADb databases demonstrate that the proposed model is more robust and accurate to extract the ROI than the popular localised and region-based ACMs. The code is available at: https://github.com/HuaxiangLiu/Localized_ERAC/.

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization.

Background: Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by employing computational intelligent techniques on radiological images like CT-Scans (Computed Tomography) of lungs. Extensive research has been reported using deep learning models to diagnose the severity of COVID-19 from CT images. This has undoubtedly minimized the manual involvement in abnormality identification but reported detection accuracy is limited. Methods: The present work proposes an expert model based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel coronavirus. In this proposed model, features are extracted from the fully connected layer of transfer learned MobileNetv2 followed by FKNN training. The hyperparameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-art methods proposed in past. Conclusion: The proposed model will help in timely and accurate identification of the coronavirus at the various phases. Such kind of rapid diagnosis will assist clinicians to manage the healthcare condition of patients well and will help in speedy recovery from the diseases. Clinical and Translational Impact Statement— The proposed automated system can provide accurate and fast detection of COVID-19 signature from lung radiographs. Also, the usage of lighter MobileNetv2 architecture makes it practical for deployment in real-time.

A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean

Abstract We present a new generalized version of the fuzzy k-nearest neighbor (FKNN) classifier that uses local mean vectors and utilizes the Bonferroni mean. We call the proposed new method Bonferroni-mean based fuzzy k-nearest neighbor (BM-FKNN) classifier. The BM-FKNN classifier can be easily fitted for various contexts and applications, because the parametric Bonferroni mean allows for problem-based parameter value fitting. The BM-FKNN classifier can perform well also in situations where clear imbalances in class distributions of data are found. The performance of the proposed classifier is tested with six real-world data sets and with one artificial data set. The results are benchmarked with classification results obtained with the classical k-nearest neighbor-, the local mean-based k-nearest neighbor-, the fuzzy k-nearest neighbor- and other three selected classifiers. In addition to this, an enhancement of the local mean-based k-nearest neighbor classifier by using the Bonferroni means is also proposed and tested. The results show that the proposed new BM-FKNN classifier has the potential to outperform the benchmarks in classification accuracy and confirm the usefulness of using the Bonferroni mean in the learning part of classifiers.

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task.

This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Originally, 150 features were extracted followed by statistical and forward feature selection. The selected features were provided to 4 classifiers, namely, K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Sets of 5 to 30 selected features were provided to the classifiers. A working point of 15 premium features resulted in accuracy values as high as 90% using the Supprt Vector Machine. The obtained results highlight the potentials of machine learning, applied to VR simulation data, to help realign the traditional apprenticeship educational paradigm to a more objective model, based on proven performance standards. Graphical abstract Using several scenarios of virtual reality neurosurgical tumor resection together with machine learning classifiers to distinguish skill level.

Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

In the recent few years, there was a concentrated search on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR consequences are dissatisfying and are still a developed research area. Finding the best feature extraction techniques and selecting an appropriate classification algorithm lead to supreme recognition accuracy and low computational overhead. This paper presents a new Arabic OCR model by integrating both of Genetic Algorithm (GA) and the Fuzzy K-Nearest Neighbor classifier (F-KNN) in a unified framework to enhance the identification accuracy. GA is utilized as a feature selection algorithm that has better convergence and spread of solutions with candid variation preservation mechanism. The F-KNN algorithm is more appropriate to classify ambiguous or uncertain data objects in the sense that every object belongs to all classes with different degrees of membership. The suggested model semantically fuses bio-inspired based feature vectors with fuzzy KNN classifier to build accurate membership function for each class. Experimental results compared to other approaches revealed the effectiveness of the suggested model and demonstrated that the feature selection approach increased the identification accuracy process.

Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier.

Skin cancer is a complex and life-threatening disease caused primarily by genetic instability and accumulation of multiple molecular alternations. Currently, there is a great interest in the prospects of image processing to provide quantitative information about a skin lesion, that can be relevance for the clinical images and also used as a stand-alone cautioning tool. To accomplish a powerful approach to recognize skin cancer without performing any unnecessary skin biopsies, this article presents a new hybrid technique for the classification of skin images using Firefly with K-Nearest Neighbor algorithm (FKNN). FKNN classifier is used to predict and classify skin cancer along with threshold-based segmentation and ABCD feature extraction. Image preprocessing and feature extraction techniques are mandatory for any image-based applications. Initially, it is essential to eliminate the illumination variation and the other unwanted shadow areas present in the skin image, which is done by homomorphic filtering called preprocessing. The comparison of our proposed method with other existing methods and a comprehensive discussion is explored based on the obtained results. The proposed FKNN provides a quantitative information about a skin lesion through hybrid KNN and firefly optimization that helps for recognizing the skin cancer efficiently than other technique with low computational complexity and time.

Complex-valued distribution entropy and its application for seizure detection

Embedding entropies are powerful indicators in quantifying the complexity of signal, but most of them are only applicable for real-valued signal and the phase information is ignored if the analyzed signal is complex-valued. To assess the complexity of complex-valued signal, a new entropy called complex-valued distribution entropy (CVDistEn) was first proposed in this study. Two rules, namely equal width criterion and equal area criterion, were employed to demarcate the complex-valued space and two kinds of CVDistEn, i.e., CVDistEn1 and CVDistEn2 were raised. Furthermore, two novel feature extraction methods: (1) flexible analytic wavelet transform (FAWT)-based CVDistEn1 and logarithmic energy (LE) (FAWTC1L), (2) FAWT-based CVDistEn2 and LE (FAWTC2L) were subsequently put forward to characterize the interictal and ictal EEGs. Fuzzy k-nearest neighbors (FKNN) classifier was finally employed to classify these two types of EEGs automatically. Experiment results show the fusion method of FAWTC1L and FKNN leads to the best accuracies (ACCs)/Matthews correlation coefficients (MCCs) of 99.99%/99.97% and 100%/100% for Bonn and Neurology & Sleep Centre EEG datasets, respectively, while the other fusion scheme of FAWTC2L and FKNN results in the highest ACCs/MCCs of 99.97%/99.93% and 99.94%/99.89% for the same datasets. The proposed methods outperform other entropy-related seizure detection schemes and most of state-of-the-art techniques, they provide another new way for automated seizure detection in EEG.

Predicting Intentions of Students for Master Programs Using a Chaos-Induced Sine Cosine-Based Fuzzy K-Nearest Neighbor Classifier

One of the essential tasks for the planning and development of talents training programs in different colleges of universities is to find how we can reasonably guide students to pursue a master's degree concerning their comprehensive situations. The purpose of this study is to develop a modified fuzzy k-Nearest Neighbor (FKNN) framework to predict the college students' intentions for master programs in advance, that is, students choose to attend the postgraduate exam or find a job after graduation. The proposed integrated framework combines the random forest (RF), FKNN, and a new chaos-enhanced sine cosine-inspired algorithm (CESCA). In this model, RF is employed to evaluate the importance of features in the dataset, while the FKNN is utilized to establish the relationship framework between the features and the college students' decisions to earn a master's degree or not. The proposed CESCA is utilized to tune the key parameters of the FKNN automatically. All eight variants of SCA have been rigorously compared based on 13 benchmark problems to validate the effectiveness of the proposed CESCA. Then, the CESCA-based FKNN (CESCA-FKNN) has been further compared against the other three classical classifiers in terms of four common performance metrics. The experimental results indicate that the proposed CESCA-FKNN can obtain the best classification accuracy. The results indicate that the established adaptive FKNN framework can be served as a powerful tool for college students' intention before pursuing a master's degree.

A parameter independent fuzzy weighted k-Nearest neighbor classifier

Unlike the canonical k-Nearest Neighbor classifier (kNN) which treat the neighbors equally, the Fuzzy k-Nearest Neighbor (FkNN) classifier imposes a weight on each of the k nearest neighbors based on their distances from the query point, by using a fuzzy membership function. FkNN though improves the performance of kNN, requires optimizing additional data dependent parameters other than k. Furthermore, FkNN does not consider the effect of those representative features of a data point which may be noisy, redundant, and may not contain useful information to distinctly identify a specific class. We attempt to address both of these issues in the current study by proposing a Parameter Independent Fuzzy class-specific Feature Weighted k-Nearest Neighbor (PIFW-kNN) classifier. PIFW-kNN formulates the issues of choosing a suitable value of k and a set of class dependent optimum weights for the features as a single-objective continuous non-convex optimization problem. We solve this problem by using a very competitive variant of Differential Evolution (DE), called Success-History based Adaptive DE (SHADE). We perform extensive experiments to demonstrate the improved accuracy of PIFW-kNN compared to the other state-of-the-art classifiers.