K-nearest Neighbor Classification Algorithm Research Articles

A new feature extraction and classification mechanisms For EEG signal processing

The Electroencephalogram (EEG) signal processing is one of the extensively used research field in recent days, in which the epileptic seizure detection and classification plays an essential role. It is very hard to determine the network of EEG signal, because it contains enormous and fluctuated information about the actions of the brain. Therefore, the earlier research works focused to detect the seizure based on its features. For this reason, it implemented various feature extraction, feature selection, and classification algorithms during the abnormality prediction. But, it has the major drawbacks of inefficient classification results, utilizing large amount of features, and increased complexity. To solve these problems, this paper aims to develop a Masking and Check-in based Feature Extraction Technique (MCFET) and an integrated K-Means with K-Nearest Neighbor classification algorithms for detecting whether the EEG signal is normal or abnormal. Initially, the features of the signal such as Signal to Noise Ratio, variance, and Standard Deviation are extracted by generating the mask value based on the check-in function. After that, the extracted features are given as the input for classification, where both the Euclidean distance and Chebyshev distance are computed. Based on the minimum similarity value, the signal is classified as normal, or abnormal (i.e. interictal or ictal). In experiments, the performance of the proposed MCFET with modified k-NN are evaluated by using different performance measures. Also, the superiority of the technique is proved by comparing it with some existing techniques.

Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure.

Heart failure (HF) is responsible for more 30-day readmissions than any other condition. Minorities, particularly African American males (AAM), are at much higher risk for readmission than the general population. In this study, demographic, social, and clinical data were collected from the electronic medical records of 132 AAM patients (control and intervention) admitted with a primary or secondary admission diagnosis of HF. Both groups received guideline-directed therapy for HF. Additionally the intervention group received a pharmacist-led intervention. Data collected from these patients were used to develop and validate a predictive model to evaluate the impact of the pharmacist-led intervention, and identify predictors of readmission in this population. After propensity score matching, the intervention was determined to have a significant impact on readmission, as a significantly smaller proportion of patients in the intervention group were readmitted as compared to the control group (11.5% vs. 42.9%; p = .03). A predictive model for 30-day readmission was developed using K-nearest neighbor (KNN) classification algorithm. The model was able to correctly classify about 71% patients with an AUROC of 0.70. Additionally, the model provided a set of key patient attributes predictive of readmission status. Among these predictive attributes was whether or not a patient received the intervention. A relative risk analysis identified that patients who received the intervention are less likely to be readmitted within 30 days. This study demonstrated the benefit of a pharmacist-led intervention for AAM with HF. Such interventions have the potential to improve quality of life for this patient population.

Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model

ABSTRACTInsurance coverage recommendation problem (ICRP) in which the most suitable coverage for customers is suggested is an essential issue for an insurance company. ICRP helps insurance companies to give suitable services to their customers. In ICRP, the insurance company tried to mine the features and records of data associated with the customers to suggest them the most economic and fitted insurance plan. The insurance companies have large databases which are considered as a proper infrastructure to analyze, model and predict the customer behavior. In this paper, a two-stage clustering-classification model is proposed to suggest suitable insurance coverage for customers. The first stage addresses a data pre-screening phase and clustering of customers based on the record of insurance coverage. Well-known clustering algorithms are employed. The superior clustering algorithm is selected based on Davies-Bouldin metric. In the second stage, several filter and wrapper methods are implemented to select proper features. The selected features are assumed as inputs of K-nearest neighbor classification algorithm. The proposed approach is applied in a real case study for clustering the customers and recommend insurance coverage. The results show that the model is capable of suggesting suitable insurance coverage based on customers’ characteristics.

Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-minute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events.

Rank-Adaptive Non-Negative Matrix Factorization

Dimension reduction is a challenge task in data processing, especially in high-dimensional data processing area. Non-negative matrix factorization (NMF), as a classical dimension reduction method, has a contribution to the parts-based representation for the characteristics of non-negative constraints in the NMF algorithm. In this paper, the NMF algorithm is introduced to extract local features for dimension reduction. Considering the problem of which NMF is required to define the number of the decomposition rank manually, we proposed a rank-adaptive NMF algorithm, in which the affinity propagation (AP) clustering algorithm is introduced to determine adaptively the number of the decomposition rank of NMF. Then, the rank-adaptive NMF algorithm is used to extract features for the original image. After that, a low-dimensional representation of the original image is obtained through the projection from the original images to the feature space. Finally, we used extreme learning machine (ELM) and k-nearest neighbor (KNN) as the classifier to classify those low-dimensional feature representations. The experimental results demonstrate that the decomposition rank determined by the AP clustering algorithm can reflect the characteristics of the original data. When it is combined with the classification algorithm ELM or KNN and applied to handwritten character recognition, the proposed method not only reduces the dimension of original images but also performs well in terms of classification accuracy and time consumption. A new rank-adaptive NMF algorithm is proposed based on the AP clustering algorithm and the original NMF algorithm. According to this algorithm, the low-dimensional representation of the original data can be obtained without any prior knowledge. In addition, the proposed rank-adaptive NMF algorithm combined with the ELM and KNN classification algorithms performs well.

The Underwater Acoustic Target Recognition Algorithm Based on Evidence Clustering

In underwater acoustic target recognition, the target signal is usually complex and the samples which are difficult to obtain also have some uncertain information. In order to effectively solve these problems, the evidence clustering recognition algorithm (TECRA) is presented. In this new method, the k-nearest neighbor are first determined by using the feature distance between the object and its neighbors in each class of the training set, and a reasonable initial basic belief assignments (bba's) for each target data are constructed by the improved k-nearest neighbor classification algorithm. Then the final global bba's of the target is obtained by optimizing the objective function of the algorithm. Finally the object can be recognized by the fusion result and the classification rule presented in the paper. Several experiments based on real underwater acoustic data sets are made to test the effectiveness of TECRA in comparison with some other methods. The results indicate that TECRA can effectively improve the recognition accuracy.

Automatic fast double KNN classification algorithm based on ACC and hierarchical clustering for big data

SummaryIn data mining, k‐nearest neighbors (KNN) classifier is an efficient lazy learning yet simple widely renowned method, which has been widely used in many actual applications, successfully. Because of time and memory restrictions of KNN, when KNN is tested in large‐scale datasets, the classification accuracy is very low. Therefore, we propose an automatic fast double KNN classification algorithm on the basis of automatically determining the cluster centers and hierarchical clustering. We introduce automatically determining the cluster centers into the KNN in training process. Namely, big data samples are divided into several parts depending on our clustering methods. Afterwards, the clusters nearest to testing samples are excavated as the new training samples in the testing process. Each of the new samples is then conducted with hierarchical clustering. In this way, computation and time complexity are greatly reduced. Finally, experiments results conducted on big data show that new KNN classification method can significantly raise the accuracy and efficiency of automatic classification than other state‐of‐the‐art KNN classification algorithms.

A hybrid approach to improve the quality of software fault prediction using Naïve Bayes and k-NN classification algorithm with ensemble method

This paper considers an improvisation in software fault prediction research area using supervised classification algorithms and it mainly focuses to increase the performance of fault prediction. In this paper, we propose a hybrid prediction model using Naive Bayes and k-nearest neighbour classification algorithm with vote ensemble method; in short it called as hNK. The goal of this model is to predict the best classification algorithm for software fault prediction based on the metrics and attributes of datasets. In the work, we have applied training sets and testing sets in hNK model with ensemble vote and we proposed the model to identify a suitable classification algorithm for fault prediction based on the accuracy and precision. We have achieved better results using hNK model for classifying supervised algorithms with different dataset.

K-Nearest Neighbor Algorithm Optimization in Text Categorization

K-Nearest Neighbor (KNN) classification algorithm is one of the simplest methods of data mining. It has been widely used in classification, regression and pattern recognition. The traditional KNN method has some shortcomings such as large amount of sample computation and strong dependence on the sample library capacity. In this paper, a method of representative sample optimization based on CURE algorithm is proposed. On the basis of this, presenting a quick algorithm QKNN (Quick k-nearest neighbor) to find the nearest k neighbor samples, which greatly reduces the similarity calculation. The experimental results show that this algorithm can effectively reduce the number of samples and speed up the search for the k nearest neighbor samples to improve the performance of the algorithm.

A New Method and a Non-Invasive Device to Estimate Anemia Based on Digital Images of the Conjunctiva

Medical practice for determining haemoglobin (Hb) concentration, which is important for anemic patients in need of blood transfusion, requires a blood sample. In this paper, we propose a non-invasive approach to Hb estimation based on the image analysis of a specific conjunctival region. Our goal is to develop a device that is not expensive and simple to use for assessing the anemic condition; this device could be used by the physician to decide whether to take a blood sample or even by a patient at home to decide whether to inform a physician; in this way, we can avoid having the patient go frequently to the laboratory to take a blood sample. This device also allows us to rapidly screen for Anemia in a large number of persons, for example, groups of girls who are at the beginning of the menses. Here, we detail the prototype of our device and the methodology for extracting key information from the color values of the acquired image. Tests conducted on 113 anemic and healthy persons show a strong correlation between the real Hb value obtained through blood sampling and the value estimated by our device. A k-nearest neighbor classification algorithm for assessing the (non)anemic condition yielded good results and helps doctors avoid a significant number of blood tests.

Recommendation system with Automated Web Usage data mining by using K-Nearest Neighbour(KNN) classification and Artificial Neural Network (ANN) algorithm

Recommendation system with Automated Web Usage data mining by using K-Nearest Neighbour(KNN) classification and Artificial Neural Network (ANN) algorithm

Novel feature selection and classification of Internet video traffic based on a hierarchical scheme

Accurate traffic classification is critical for efficient network management and resources utilization. Different video traffics have different QoS (Quality of Service) requirements. To provide Internet video services with better QoS support, a fine grained classification scheme for network video traffic is proposed in this paper. Through extensive statistical analysis of typical video traffic flows with a consistency-based method, several new flow statistical features are extracted. They are found to be more effective in discriminating different video traffics, especially from the QoS perspective, than commonly used features available in the literature. A hierarchical k-Nearest Neighbor (kNN) classification algorithm is then developed based on the combinations of these statistical features. Experiments are performed to evaluate the effectiveness of the proposed method on a large scale real network video traffic data. The experimental results show that the proposed method outperforms existing methods applying commonly used flow statistical features.

RETRACTED ARTICLE: Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

Obstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann–Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.

Multi-label classification algorithm research based on swarm intelligence

Since data and resources have massive feature and feature of data are increasingly complex, traditional data structures are not suitable for current data anymore. Therefore, traditional single-label learning method cannot meet the requirements of technology development and the importance of multi-label leaning method becomes more and more highlighted. K-Nearest Neighbor (KNN) classification method is a lazy learning method in data classification methods. It does not need data training process and theoretical system is mature. In addition, principle and implementation is simple. This paper proposed improvements strategies only considers numerical feature of sample KNN when classifying, but not consider the disadvantage of sample structure feature. This paper introduced particle swarm optimization algorithm into KNN classification and make adjustments to Euclidean distance formula in traditional KNN classification algorithm and add weight value to each feature. Using adjusted distance formula to train training data through particle swarm optimization algorithm and optimized a set of weight value for all features and put these optimized weight values to adjusted distance formula and calculated the distance between each example in test data set and in training data set and predict the test data set. Experiment results show that weighted KNN classification algorithm based on particle swarm optimization algorithm can achieve better classification accuracy than traditional KNN classification algorithm.

Hierarchical Classification with Jumping Emerging Patterns

Classification is a common task in Machine Learning and Data Mining. Some classification problems are called hierarchical classification problems because they need to take into account a hierarchical taxonomy which establishes an order between involved classes. The protein's function prediction is considered a hierarchical classification problem because their functions are arranged in a hierarchical taxonomy of classes. This paper presents an algorithm for hierarchical classification using the jumping emerging patterns approach. Jumping emerging patterns have been used to flat classification and in this work we explore its adoption in a hierarchical classification scenario. The proposed algorithm was evaluated in eight real datasets, compared against two other recent hierarchical classification algorithms from the literature and also with flat k-nearest neighbor classification algorithm. Preliminary results showed that the proposed approach is an alternative for hierarchical classification, having as main advantages the simplicity and understandability with good accuracy.

RETRACTED ARTICLE: Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques

It is extremely significant to identify sleep stages accurately in the diagnosis of obstructive sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a practical and applicable method. For this purpose , the signal of heart rate variability (HRV) has been derived from photoplethysmography (PPG). Feature extraction has been made from PPG and HRV signals. Afterward, the features, which will represent sleep and wakefulness in the best possible way, have been selected using F-score feature selection method. The selected features were classified with k-nearest neighbors classification algorithm and support vector machines. According to the results of the classification, the classification accuracy rate was found to be 73.36 %, sensivity 0.81, and specificity 0.77. Examining the performance of the classification, classifier kappa value was obtained as 0.59, area under an receiver operating characteristic value as 0.79, tenfold cross-validation as 77.35 %, and F-measurement value as 0.79. According to the results accomplished, it was concluded that PPG and HRV signals could be used for sleep staging process. It is a great advantage that PPG signal can be measured more practically compared to the other sleep staging signals used in the literature. Improving the systems, in which these signals will be used, will make diagnosis methods more practical.

Real-Time Model-Based Fault Detection of Continuous Glucose Sensor Measurements.

Faults in subcutaneous glucose concentration readings with a continuous glucose monitoring (CGM) may affect the computation of insulin infusion rates that can lead to hypoglycemia or hyperglycemia in artificial pancreas control systems for patients with type 1 diabetes (T1D). Multivariable statistical monitoring methods are proposed for detection of faults in glucose concentration values reported by a subcutaneous glucose sensor. A nonlinear first principle glucose/insulin/meal dynamic model is developed. An unscented Kalman filter is used for state and parameter estimation of the nonlinear model. Principal component analysis models are developed and used for detection of dynamic changes. K-nearest neighbor classification algorithm is used for diagnosis of faults. Data from 51 subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with 84.2% sensitivity. Overall, 155 (out of 184) of the CGM failures are detected with a 2.8-min average detection time. A novel algorithm that integrates data-driven and model-based methods is developed. The proposed method is able to detect CGM failures with a high rate of success. The proposed fault detection algorithm can decrease the effects of faults on insulin infusion rates and reduce the potential for hypo- or hyperglycemia for patients with T1D.

Multi-view Ensemble Learning Using Optimal Feature Set Partitioning: An Extended Experiments and Analysis in Low Dimensional Scenario

Abstract Multi-view ensemble learning (MEL) has successfully addressed the issue related to high dimensionality of the data. It exploits the information of views of the data. To obtain views of data, an optimal feature set partitioning (OFSP) method [1] has been shown performance enhancement of MEL. Results of the experiments carried out on datasets and their statistical analysis show the effectiveness for classification problem in high dimensional scenario. In this work, classification performance of MEL using OFSP method has been analyzed in low dimensional situations. Therefore, experiments are performed on low dimension datasets using K-Nearest Neighbor (KNN), Naive Bayesian (NB) and Support Vector Machine (SVM) classification algorithms. The experimental results and their statistical analysis show that OFSP method is also effective in low dimensional environment.

Multi-label segmentation of white matter structures: Application to neonatal brains

Accurate and consistent segmentation of brain white matter bundles at neonatal stage plays an important role in understanding brain development and detecting white matter abnormalities for the prediction of psychiatric disorders. Due to the complexity of white matter anatomy and the spatial resolution of diffusion-weighted MR imaging, multiple fiber bundles can pass through one voxel. The goal of this study is to assign one or multiple anatomical labels of white matter bundles to each voxel to reflect complex white matter anatomy of the neonatal brain. For this, we develop a supervised multi-label k-nearest neighbor (ML-kNN) classification algorithm in Riemannian diffusion tensor spaces. Our ML-kNN considers diffusion tensors lying on the Log-Euclidean Riemannian manifold of symmetric positive definite (SPD) matrices and their corresponding vector space as feature space. The ML-kNN utilizes the maximum a posteriori (MAP) principle to make the prediction of white matter labels by reasoning with the labeling information derived from the neighbors without assuming any probabilistic distribution of the features. We show that our approach automatically learns the number of white matter bundles at a location and provides anatomical annotation of the neonatal white matter. In addition, our approach also provides the binary mask for individual white matter bundles to facilitate tract-based statistical analysis in clinical studies. We apply this method to automatically segment 13 white matter bundles of the neonatal brain and examine the segmentation accuracy against semi-manual labels derived from tractography.