Fuzzy K-nearest Neighbor Method Research Articles

Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Disiase Classification

Dengue hemorrhagic fever (DHF) is a condition brought on by infection with the dengue virus. DHF is a severe illness with hemorrhagic clinical signs that can result in shock and death. One of the four viral serotypes of the genus Flavivirus is responsible for DHF. DHF symptoms include fever, joint pain, red skin patches, and others that are similar to those of other illnesses. So that there are no errors in illness prediction, strong accuracy and accuracy are required when classifying DHF patients or not. The Fuzzy K-Nearest Neighbor (FKNN) method is used in this study to classify dengue sickness in order to obtain the best classification outcomes. In this investigation, k was searched for eight times, with values of 3,5,7,9,11,13,15, and 20. Each K's accuracy statistics are 75.15, 75.16, 77.58%, 79.51%, 85.01%, 78.14%, and 75.20 percent. K = 13, which has an accuracy score of 85.01%, yields the highest accuracy.

Pengenalan Pola Garis Telapak Tangan Menggunakan Metode Fuzzy K-Nearest Neighbor

Palm pattern recognition is an important topic in the field of pattern recognition and biometrics. This study aims to develop a palm line pattern recognition method using the Fuzzy K-Nearest Neighbor (Fuzzy KNN) method to improve the accuracy and effectiveness of the individual recognition system. The research was conducted by collecting images, performing segmentation, feature extraction, and implementing the Fuzzy KNN method. Based on the results of the experiments that have been carried out, it can be seen that the average accuracy obtained is 20%, which means that the system is less able to recognize palm line patterns. This is due to a lack of dataset which causes poor recognition performance on the model.

Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm

Alzheimer's disease (AD) is a typical senile degenerative disease that has received increasing attention worldwide. Many artificial intelligence methods have been used in the diagnosis of AD. In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of AD, so as to distinguish between patients with mild cognitive impairment (MCI), Alzheimer's disease (AD) and normal controls (NC). First, the performance and feature selection accuracy of the method are validated on 5 different benchmark datasets. Secondly, the paper uses the Structural Magnetic Resolution Imaging (sMRI) dataset, in terms of classification accuracy, sensitivity, specificity, etc., the effectiveness of the method on the AD dataset is verified. The simulation results show that the classification accuracy of this method for AD and MCI, AD and NC, MCI and NC are 95.37%, 100%, and 93.95%, respectively. These accuracies are better than the other five comparison methods. The method proposed in this paper can learn better feature subsets from serial multimodal features, so as to improve the performance of early AD diagnosis. It has a good application prospect and will bring great convenience for clinicians to make better decisions in clinical diagnosis.

LECTURERS ADMISSIONS SELECTIONS MODEL USING FUZZY K-NEAREST NEIGHBOR METHOD

Higher Education, or tertiary education, is the final stage which is optional in formal education. It is usually organized in the form of a university, academy, seminary, high school, or institute. Every tertiary institution needs qualified and professional educators because they have an important role in the process of implementing the Tri Dharma of Higher Education. Recruitment for teaching staff usually has several stages and standardization of assessment in selection proces. In order for the process of selecting educators to be carried out objectively, a support system is need to carry out the assessment process. This study applies the Fuzzy K-Nearest Neighbor (FK-NN) method for the classification process in determining prospective educators who pass or not. Data classification is a new data or object grouping into classes or labels based on certain attributes. The application of the FK-NN method has several stages, namely weighting the criteria, then calculating the closeness of the test data and training data, finding the value of k-nearest neighbors between the training data and testing data and determining the membership of each data. Tests were carried out using the Confusion matrix method on several variations of the k value where the highest percentage was obtained from the value of k = 5. The test results for all k values obtained an average accuracy rate of 89.22%, 89.22% precision and 82.45% recall with 114 training data and 50 test data. Based on the average value of the test results, it can be concluded that the FK-NN method is feasible and good to use for the selection of educators with the classification of pass or not.

Fuzzy K-Nearest Neighbor Based Dental Fluorosis Classification Using Multi-Prototype Unsupervised Possibilistic Fuzzy Clustering via Cuckoo Search Algorithm.

Dental fluorosis in children is a prevalent disease in many regions of the world. One of its root causes is excessive exposure to high concentrations of fluoride in contaminated drinking water during tooth formation. Typically, the disease causes undesirable chalky white or even dark brown stains on the tooth enamel. To help dentists screen the severity of fluorosis, this paper proposes an automatic image-based dental fluorosis segmentation and classification system. Six features from red, green, and blue (RGB) and hue, saturation, and intensity (HIS) color spaces are clustered using unsupervised possibilistic fuzzy clustering (UPFC) into five categories: white, yellow, opaque, brown, and background. The fuzzy k-nearest neighbor method is used for feature classification, and the number of clusters is optimized using the cuckoo search algorithm. The resulting multi-prototypes are further utilized to create a binary mask of teeth and used to segment the tooth region into three groups: white-yellow, opaque, and brown pixels. Finally, a fluorosis classification rule is created based on the proportions of opaque and brown pixels to classify fluorosis into four classes: Normal, Stage 1, Stage 2, and Stage 3. The experimental results on 128 blind test images showed that the average pixel accuracy of the segmented binary tooth mask was 92.24% over the four fluorosis classes, and the average pixel accuracy of segmented teeth into white-yellow, opaque, and brown pixels was 79.46%. The proposed method correctly classified four classes of fluorosis in 86 images from a total of 128 blind test images. When compared with a previous work, this result also indicates 10 out of 15 correct classifications on the blind test images, which is equivalent to a 13.33% improvement over the previous work.

Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm

The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully studies the main parameters affecting the performance of the sine and cosine algorithm, integrates the reverse learning algorithm, adds an elite opposition solution and forms the hybrid sine and cosine algorithm (hybrid SCA). Combined with the fuzzy k-nearest neighbor method and the hybrid SCA, this paper numerically simulates two-class datasets and multi-class datasets, obtains a large number of numerical results and analyzes the results. The hybrid SCA FKNN proposed in this paper has achieved good accuracy in classification and prediction results under 10 different types of data sets. Compared with SCA FKNN, LSCA FKNN, BA FKNN, PSO FKNN and SSA FKNN, the prediction accuracy is significantly improved. In the Wilcoxon signed rank test with SCA FKNN and LSCA FKNN, the zero hypothesis (significance level 0.05) is rejected and the two classifiers have a significantly different accuracy.

Enhanced K-Nearest Neighbor (KNN) Algorithm for classification of big data

Due to the enormous increase in the size of the data, it becomes wearisome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value, and complexity. The application of data mining has yielded positive results in the information gathering of big data. However, the current growth of technology had made it very difficult for software developers and data analysts to gather information on Big Data using existing data mining algorithms such as K-Nearest Neighbour (KNN), K-means, Support Vector Machines (SVM), Apriori, Page-rank, and AdaBoost. In this paper, we implemented the Fuzzy K-Nearest Neighbor method using the Map-Reduce paradigm to process big data. Results on different data sets show that the proposed Fuzzy K-Nearest Neighbor method outperforms a better performance than the method reviewed in the literature.

Identification of Authenticity and Nominal Value of Indonesia Banknotes Using Fuzzy KNearest Neighbor Method

The existence of counterfeit money is often troubling the public. The solution given by the government to be careful of counterfeit money is by means of 3D (seen, touched and looked at). However, this step has not been perfectly able to distinguish real money and fake money. So there is a need for a system to help detect the authenticity of money. Therefore, in this study a system was designed that can detect the authenticity of rupiah and its nominal value. For data acquisition, this system uses detection boxes, ultraviolet lights and smartphone cameras. As for feature extraction, this system uses segmentation methods. The segmentation method based on the threshold value is used to obtain an invisible ink pattern which is a characteristic of real money along with the nominal value of the money. The feature is then used in the stage of detection of money authenticity using FKNN (Fuzzy K-Nearest Neighbor) method. From 24 test data, obtained an average accuracy of 96%. This shows that the system built can detect the authenticity and nominal value of the rupiah well.

Classification of Milk Fish Quality using Fuzzy K-Nearest Neighbor Method Based on Form Descriptor and Co-Occurrence Matrix

Milkfish is a type of fish that lives in fresh water. Caught milkfish will be sorted at fish processing companies and will be a variety of milk, fish products in the form of cooked or in the form of fresh fish. A manual separation system of milkfish often experiences errors in the separation of fish species so that it takes a long time in the process of separating these types of fish, because it is very dependent on the skills of employees or employees. Errors in the separation of milkfish based on their size will cause a failure in the production section because it will affect the difference in the maturity level of a product, which will result in errors in the process of cooking the fish, which has an impact on the loss in the financial sector of a company. In order to maintain the quality of milkfish and speed up the sorting of milkfish, it is necessary to create a system that can help workers and employers in distinguishing the quality of milkfish based on their size. So the writer will create a system “Image Quality Classification of Milkfish Using Fuzzy K-Nearest Neighbor Method Based on Descriptors of Forms and Co-occurrence Testing was carried out using 45 images of milk fish. From the test results based on the shape feature and the co-assurance matrix produces 100%, which is in accordance with the form feature requirements and the co-insurance matrix as the reference data.

Regularized Fuzzy Discriminant Analysis for Hyperspectral Image Classification With Noisy Labels

Numerous studies have been conducted for hyperspectral image (HSI) classification by assuming that the label information of training data is fully available and correct. However, such an assumption may not always be true in practical applications, which could impact feature extraction methods and eventually compromise the performance of hyperspectral image classification. To address this issue in hyperspectral image classification, we propose a Regularized Fuzzy Discriminant Analysis (RFDA) based feature extraction method to effectively utilize the spatial and spectral information of HSIs with noisy labels. Firstly, the physical properties of HSIs are explored to reconstruct the data. Secondly, the labeled training samples and their unlabeled spatial neighborhood samples are fuzzified using the Fuzzy K-Nearest Neighbor (FKNN) method. Finally, a regularization term using a Fuzzy Locality Preserving Scatter (FLPS) matrix is integrated into fuzzy discriminant analysis, and the spatial-spectral information of HSIs is effectively fused to construct the projection matrix. As a result, the proposed method not only corrects the mislabeled samples effectively, but also preserves the neighborhood relationship among the pixels in the spatial domain and the fundamental structure among the samples in the spectral-domain, which is beneficial for hyperspectral image classification. Experimental results on three synthetic datasets and three public hyperspectral datasets show that our proposed RFDA method outperforms several state-of-the-art feature extraction methods in terms of classification accuracy.

An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach.

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

Sistem Pengenalan Bahasa Isyarat Indonesia dengan Menggunakan Metode Fuzzy K-Nearest Neighbor

The Indonesian Natural Sign System (SIBI) is one of the most natural languages of communication, especially for deaf and speech impaired. Deaf and speech impaired can understand and communicate with each other by using sign language, but some normal people will have difficulty understanding sign language with deaf and speech impunity to say. To overcome these problems need develop a system that is able to recognize the Indonesian Sign System (SIBI) which is expected capable of learning media in communicating between the deaf and normal humans. The introduction of the Indonesian Sign System (SIBI) will consists of three main stages: image acquisition, preprocessing and recognition. In this research the classification method used is Fuzzy KNearest Neighbor (FKNN) method. Based on the results of experiments conducted with the classification using the method Fuzzy K-Nearest Neighbor (FKNN) obtained an accuracy of 88%.
 Index Term— Fuzzy K-Nearest Neighbor, Sistem Isyarat Bahasa Indonesia (SIBI).

Perbaikan Kualitas Citra Untuk Klasifikasi Daun Menggunakan Metode Fuzzy K-Nearest Neighbor

Plants have many benefits for human life such as food, medicine, industry, environmental protection, even oxygen provider for other organisms. To know the types of plants is necessary. Classification of plants can be done with additional features of leaves in these plants. In determining whether or not the image identification process is needed a process of image quality improvement. Improved image quality is used to prepare the image in an ideal form so as not to cause problems and interpellation results as well. In this research the method used is Fuzzy K-Nearest Neighbor (FKNN) method. The Fuzzy K-Nearest Neighbor (FKNN) method is the most objective method. Based on the results of experiments conducted, Fuzzy K - Nearest Neighbor (FKNN) modeling method was obtained for 93% completeness.
 Keywords-Image quality improvement, Fuzzy KNearest Neighbor (FKNN)

Big Data Classification using Fuzzy K-Nearest Neighbor

becomes troublesome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this paper is to classify big data using Fuzzy K-Nearest Neighbor classifier, and to provide a comparative study between the results of the proposed systems and the method reviewed in the literature. In this paper we implemented the Fuzzy KNearest Neighbor method using the MapReduce paradigm to process on big data. Results on different data sets show that the proposed Fuzzy K-Nearest Neighbor method outperforms a better performance than the method reviewed in the literature.

English

The aim of our research is to classify digital mammograms into two classes, abnormal microcalcification and normal. Texture is one of the major mammographic characteristics. The statistical textural of Gray Level Coocurrence Matrix (GLCM) used in characterizing images are contrast, energy and entropy. K-Nearest Neighbor (K-NN) and Fuzzy K-Nearest Neighbor (FK-NN) was proposed for classifying images. The result of K-NN method shows that 77.78% accuracy , 50.00% sensitifity and 100% specifisity. The result of FK-NN method shows that 88.89% accuracy , 100% sensitifity and 80.00% specifisity. .

A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method

Bankruptcy prediction is one of the most important issues in financial decision-making. Constructing effective corporate bankruptcy prediction models in time is essential to make companies or banks prevent bankruptcy. This study proposes a novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor (FKNN) method, where the neighborhood size k and the fuzzy strength parameter m are adaptively specified by the continuous particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is also utilized to choose the most discriminative subset of features for prediction. Adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. Moreover, both the continuous and binary PSO are implemented in parallel on a multi-core platform. The proposed bankruptcy prediction model, named PTVPSO-FKNN, is compared with five other state-of-the-art classifiers on two real-life cases. The obtained results clearly confirm the superiority of the proposed model in terms of classification accuracy, Type I error, Type II error and area under the receiver operating characteristic curve (AUC) criterion. The proposed model also demonstrates its ability to identify the most discriminative financial ratios. Additionally, the proposed model has reduced a large amount of computational time owing to its parallel implementation. Promisingly, PTVPSO-FKNN might serve as a new candidate of powerful early warning systems for bankruptcy prediction with excellent performance.

Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method

Amphiphilic pseudo-amino acid composition (Am-Pse-AAC) with extra sequence-order information is a useful feature for representing enzymes. This study first utilizes the k-nearest neighbor ( k-NN) rule to analyze the distribution of enzymes in the Am-Pse-AAC feature space. This analysis indicates the distributions of multiple classes of enzymes are highly overlapped. To cope with the overlap problem, this study proposes an efficient non-parametric classifier for predicting enzyme subfamily class using an adaptive fuzzy r-nearest neighbor (AFK-NN) method, where k and a fuzzy strength parameter m are adaptively specified. The fuzzy membership values of a query sample Q are dynamically determined according to the position of Q and its weighted distances to the k nearest neighbors. Using the same enzymes of the oxidoreductases family for comparisons, the prediction accuracy of AFK-NN is 76.6%, which is better than those of Support Vector Machine (73.6%), the decision tree method C5.0 (75.4%) and the existing covariant-discriminate algorithm (70.6%) using a jackknife test. To evaluate the generalization ability of AFK-NN, the datasets for all six families of entirely sequenced enzymes are established from the newly updated SWISS-PROT and ENZYME database. The accuracy of AFK-NN on the new large-scale dataset of oxidoreductases family is 83.3%, and the mean accuracy of the six families is 92.1%.

Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method

The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins. Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ jul@ssu.ac.kr.