Traditional KNN Algorithm Research Articles

Classification of Beef and Pork Images Based on Color Features and Pseudo Nearest Neighbor Rule

This research is motivated by the need for halal foods in Muslim society with the purpose of avoiding non-halal foods, such as pork, that are sold in the market. Although beef and pork basically have different characteristics, not all Muslims know the differences. Moreover, people nowadays sell beef mixed with pork to obtain more profits. Hence, this paper proposed the implementation of the Pseudo-Nearest Neighbor Rule (PNNR) in classifying images of beef and pork slices based on color features. Based on the image dataset that has been collected, the very significant difference that can be identified visually between beef and pork is the color. The color features were extracted from the image using a color histogram from two different color channels, RGB and HSV. As the result, PNNR that used color features from the RGB channel achieved up to 87.43% accuracy, while using the HSV channel, it can reach up to 93.78% of accuracy. Additionally, this paper evaluates the stability of the proposed method by assessing the variance of classification accuracy across different values of k. It is also noticed that PNNR’s performance is relatively consistent for various values of k compared to the traditional kNN algorithm.

Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

Comparative Study of Physical Education Teaching in Middle Schools at Home and Abroad Using Clustering Algorithm.

Physical education in middle school is very important for teenagers, so it is also crucial to understand the differences between PET (Physical Education Teaching) systems in middle schools at home and abroad. The frontier and hotspot of PET research in middle schools at home and abroad are examined in this paper using citation analysis, information visualization, and cluster analysis, as well as CiteSpace software. The findings show that PET method research in China is qualitative, whereas PET method research in middle schools around the world is quantitative evaluation and empirical research. Domestic research hotspots focus on classroom instructional design, whereas foreign countries focus on load identification theory's application in instructional design. Frontier research in the United States is dispersed and covers a wide range of topics, whereas research in other countries focuses on cognitive load theory. The classification time of this improved algorithm is reduced by 190.97 seconds when compared to the traditional KNN algorithm, and the total time is increased by more than 50%. According to the findings, nonsports or nonsports influencing factors should be given more consideration in the study of adolescent physical fitness decline in China.

Multispectral Remote Sensing Data Analysis Based on KNNLC Algorithm and Multimedia Image

In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. It is found that the KNN algorithm is one of the classic algorithms of data mining. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. However, the algorithm inevitably has many problems, such as how to determine the appropriate K value, the unsatisfactory effect of data processing for some special distributions, and the unacceptable computational complexity of high-dimensional data. In order to solve these shortcomings, the researchers proposed the KNNLC algorithm. Then, taking the classification experiment as an example, through the comparison of the experimental results on different data sets, it is proved that the average level of the classification performance of the KNNLC algorithm is better than the classic KNN classification algorithm. The KNNLC algorithm shows better performance in most cases, with an accuracy rate of 2 to 5 percentage points higher. An improved algorithm is proposed for the nearest neighbor selection strategy of the traditional KNN algorithm. First, in theory, combined with the theory of sparse coding and locally constrained linear coding, the classical KNN algorithm is improved, and the KNNLC algorithm is proposed. The comparison of the experimental results on the data set proves that the average level of the KNNLC algorithm is better than the classical KNN classification algorithm in terms of classification performance.

Multi-core fiber channel equalization algorithm based on K nearest neighbor method

Aiming at the channel damage problems of multi-core fiber (Multi-core Fiber, MCF), the AKNN (Adapted K-Nearest Neighbor) algorithm is proposed. The MATLAB software was used to simulate the channel of the seven-core fiber, both the inter-core crosstalk (Crosstalk, XT) and nonlinear effects were considered. Then the KNN algorithm was adapted to equalize the damaged signal at the receiver. The simulation results show that the AKNN algorithm possesses better BER performance and lower OSNR requirements with the extension of transmission distance, compared to traditional KNN algorithm.

Application of English Vocabulary Presentation Based on Clustering in College English Teaching

As the most basic element in English learning, vocabulary has always been the focus of teaching in college English classes, but the teaching effect is often unsatisfactory. In this paper, the genetic algorithm fitness function design part is integrated with the K-medoids algorithm to form K-GA-medoids, and secondly, it is combined with KNN to form an algorithmic framework for English vocabulary classification. In the classification process, clustering and classification steps are taken to realize the reduction of the training set samples and thus reduce the computational overhead. The experiments show that K-GA-medoids have significantly improved the clustering effect compared with traditional K-medoids, and the combination of K-GA-medoids and KNNs has effectively improved the efficiency of English vocabulary classification compared with the traditional KNN algorithm, while ensuring the classification accuracy. We found that students in college English course consider word memorization as a difficult learning task, and the traditional vocabulary teaching methods are not very effective, and the knowledge of etymology is often little known and rarely covered in classroom lectures. Therefore, the article explores new ideas and strategies for teaching vocabulary in college English from the perspective of etymology.

Tumor imaging diagnosis analysis based on improved KNN algorithm

As a simple, effective and non-parameter analysis method, knn is widely used in text classification, image recognition, etc. [1]. However, this method requires a lot of calculations in practical applications, and the uneven distribution of training samples will directly lead to a decrease in the accuracy of tumor image classification. To solve this problem, we propose a method based on dynamic weighted KNN to improve the accuracy of classification, which is used to solve the problem of automatic prediction and classification of medical tumor images based on image features and automatic abnormality detection. According to the classification of tumor image characteristics, it can be divided into two categories: benign and malignant. This method can assist doctors in making medical diagnosis and analysis more accurately. The experimental results show that this method has certain advantages compared with the traditional KNN algorithm.

Image Classification Method Based on Improved KNN Algorithm

As the development of machine vision technology, artificial intelligence algorithms are gradually popularized for identifying images. However, traditional KNN algorithm actually costs too much time when classifying images, which is not qualified to actual application scenes. An improved algorithm is proposed in the paper. The test time has been greatly shortened and the efficiency of KNN algorithm is improved by increasing the screening of data sets. By setting STM32F103 as master control and OV7670 as camera, actual detection of volleyball, football, and basketball was carried out after test environment was set up. And the test time is shorter compared with that of general KNN algorithm. At the same time, the identification accuracy is high, which indicates that the method has good practicability.

Influencing Factors and Forecasting Statistics of Enterprise Market Sales Based on Big Data and Intelligent IoT

With the acceleration of economic development, enterprise management is facing more severe challenges. Big data analysis based on the intelligent Internet of Things (IoT) has a positive effect on the development of enterprise management and can make up for the shortcomings of enterprise management. In this paper, we develop a big data processing method based on intelligent IoT which can mine the factors that affect the company’s market sales from the collected data. Then, we propose a KNN classification algorithm based on overlapping k‐means clustering. This algorithm adds a training process to the traditional KNN algorithm, which can accurately classify data and greatly improve the efficiency of the classification algorithm. Numerical analysis results prove the effectiveness of the proposed algorithm.

EKNN: Ensemble classifier incorporating connectivity and density into kNN with application to cancer diagnosis

In the microarray-based approach for automated cancer diagnosis, the application of the traditional k-nearest neighbors kNN algorithm suffers from several difficulties such as the large number of genes (high dimensionality of the feature space) with many irrelevant genes (noise) relative to the small number of available samples and the imbalance in the size of the samples of the target classes. This research provides an ensemble classifier based on decision models derived from kNN that is applicable to problems characterized by imbalanced small size datasets. The proposed classification method is an ensemble of the traditional kNN algorithm and four novel classification models derived from it. The proposed models exploit the increase in density and connectivity using K1-nearest neighbors table (KNN-table) created during the training phase. In the density model, an unseen sample u is classified as belonging to a class t if it achieves the highest increase in density when this sample is added to it i.e. the unseen sample can replace more neighbors in the KNN-table for samples of class t than other classes. In the other three connectivity models, the mean and standard deviation of the distribution of the average, minimum as well the maximum distance to the K neighbors of the members of each class are computed in the training phase. The class t to which u achieves the highest possibility of belongness to its distribution is chosen, i.e. the addition of u to the samples of this class produces the least change to the distribution of the corresponding decision model for class t. Combining the predicted results of the four individual models along with traditional kNN makes the decision space more discriminative. With the help of the KNN-table which can be updated online in the training phase, an improved performance has been achieved compared to the traditional kNN algorithm with slight increase in classification time. The proposed ensemble method achieves significant increase in accuracy compared to the accuracy achieved using any of its base classifiers on Kentridge, GDS3257, Notterman, Leukemia and CNS datasets. The method is also compared to several existing ensemble methods and state of the art techniques using different dimensionality reduction techniques on several standard datasets. The results prove clear superiority of EKNN over several individual and ensemble classifiers regardless of the choice of the gene selection strategy.

Modified K-nearest Neighbor Algorithm with Variant K Values

In Machine Learning K-nearest Neighbor is a renowned supervised learning method. The traditional KNN has the unlike requirement of specifying ‘K’ value in advance for all test samples. The earlier solutions of predicting ‘K’ values are mainly focused on finding optimal-k-values for all samples. The time complexity to obtain the optimal-k-values in the previous method is too high. In this paper, a Modified K-Nearest Neighbor algorithm with Variant K is proposed. The KNN algorithm is divided in the training and testing phase to find K value for every test sample. To get the optimal K value the data is trained for various K values with Min-Heap data structure of 2*K size. K values are decided based on the percentage of training data considered from every class. The Indian Classical Music is considered as a case study to classify it in different Ragas. The Pitch Class Distribution features are input to the proposed algorithm. It is observed that the use of Min-Heap has reduced the space complexity nonetheless Accuracy and F1-score for the proposed method are increased than traditional KNN algorithm as well as Support Vector Machine, Decision Tree Classifier for Self-Generated Dataset and Comp-Music Dataset.

Application Research of KNN Algorithm Based on Clustering in Big Data Talent Demand Information Classification

With the growth of massive data in the current mobile Internet, network recruitment is gradually growing into a new recruitment channel. How to effectively mine available information in the massive network recruitment data has become the technical bottleneck of current education and social supply and demand development. The renewal of talent demand information is carried out every day, which produces a large amount of text data. How to manage these talents’ demand information reasonably becomes more and more important. Artificial classification is time-consuming and laborious, which is unrealistic naturally. Therefore, using automatic text categorization technology to classify and manage this information becomes particularly important. To break through the bottleneck of this technology, a heuristic KNN text categorization algorithm based on ABC (artificial bee colony) is proposed to adjust the weight of features, and the similarity between test observation and training observation is measured by using the method of fuzzy distance measurement. Firstly, the recruitment information is segmented and feature selection and noise data elimination are carried out by using term frequency-inverse document frequency (TF-IDF) algorithm and AP (affinity propagation) clustering algorithm. Finally, the text information is classified by using KNN algorithm combined with heuristic search and fuzzy distance measurement. The experimental results show that this method effectively solves the problem of poor stability and low classification accuracy of traditional KNN algorithm in text categorization method for talent demand.

Health Data Driven on Continuous Blood Pressure Prediction Based on Gradient Boosting Decision Tree Algorithm

Diseases related to issues with blood pressure are becoming a major threat to human health. With the development of telemedicine monitoring applications, a growing number of corresponding devices are being marketed, such as the use of remote monitoring for the purposes of increasing the autonomy of the elderly and thus encouraging a healthier and longer health span. Using machine learning algorithms to measure blood pressure at a continuous rate is a feasible way to provide models and analysis for telemedicine monitoring data and predicting blood pressure. For this paper, we applied the gradient boosting decision tree (GBDT) while predicting blood pressure rates based on the human physiological data collected by the EIMO device. EIMO equipment-specific signal acquisition includes ECG and PPG. In order to avoid over-fitting, the optimal parameters are selected via the cross-validation method. Consequently, our method has displayed a higher accuracy rate and better performance in calculating the mean absolute error evaluation index than methods, such as the traditional least squares method, ridge regression, lasso regression, ElasticNet, SVR, and KNN algorithm. When predicting the blood pressure of a single individual, calculating the systolic pressure displays an accuracy rate of above 70% and above 64% for calculating the diastolic pressure with GBDT, with the prediction time being less than 0.1 s. In conclusion, applying the GBDT is the best method for predicting the blood pressure of multiple individuals: with the inclusion of data such as age, body fat, ratio, and height, algorithm accuracy improves, which in turn indicates that the inclusion of new features aids prediction performance.

A classification algorithm based on weighted ML-kNN for multi-label data

The ML-kNN algorithm uses naive Bayesian classification to modify the traditional kNN algorithm to solve multi-label classification problems. However, the ML-kNN algorithm is prone to misjudgement or incomplete judgment of the unseen instance's label set in two special cases: when the number of labels in the training set is not balanced and when the training instances are unevenly distributed in space. Therefore, a weighted ML-kNN algorithm (i.e., wML-kNN) is proposed in this paper. The main idea is to assign different weights to each label according to the proportion of labels and mutual information of the spatial distribution of unseen instances to training instances. This method can reduce the probability of misjudgement of the unseen instance's label set. A comparative study was conducted on four multi-label datasets that included review classification and three other published benchmark multi-label datasets: yeast gene function analysis, natural scene classification, and musical sentiment classification. The results show that the performance of the wML-kNN algorithm is better than the other four multi-label learning algorithms, including ML-kNN.

An Intrusion Detection Model based on Hybrid Classification algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.

A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification

k nearest neighbors (kNN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based kNN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an R-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new kNN algorithm and its improvements to other version of kNN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional kNN algorithm, the proposed manifold version kNN shows promising potential for classifying manifold-distributed data.

KEFCM: kNN-Based Dynamic Evolving Fuzzy Clustering Method

Despite the recent emergence of research, creating an evolving fuzzy clustering method that intelligently copes with huge amount of data streams in the present high-speed networks involves a lot of difficulties. Several efforts have been devoted to enhance traditional clustering techniques into on-line evolving fuzzy able to learn and develop continuously. In line with these efforts, we propose kEFCM, kNN-based evolving fuzzy clustering method. kEFCM overcomes the problems of computational cost, dynamic fuzzy evolving, and clustering complexity of traditional kNN. It employs the least-squares method in determining the cluster center and influential area, as well as the Euclidean distance in identifying the membership degree. It enhances the traditional kNN algorithm by involving only cluster centers in making classification decisions and evolving on-line the clusters when a new data arrives. For evaluation purpose, the experimental results on a collection of benchmark datasets are compared against other well-known clustering methods. The evaluation results approve a good competitive level of kEFCM.

A Lexical Approach for Text Categorization of Medical Documents

This research proposes a novel lexical approach to text categorization in the bio-medical domain. We have proposed LKNN (Lexical KNN) algorithm, in which lexemes (tokens) are used to represent the medical documents. These tokens are used to classify the abstracts by matching them with the standard list of keywords specified as MESH (Medical Subject Headings). It automatically classifies journal articles of medical domain into specific categories. We have used the collection of medical documents, called Ohsumed, as the test data for evaluating the proposed approach. The results show that LKNN outperforms the traditional KNN algorithm in terms of standard F-measure.

The Nearest Neighbor Algorithm of Filling Missing Data Based on Cluster Analysis

Missing data universally exists in various research fields and it results in bad computational performance and effcet. In order to improve the accuracy of filling in the missing data, a filling missing data algorithm of the nearest neighbor based on the cluster analysis is proposed by this paper. After clustering data analysis,the algorithm assigns weights according to the categories and improves calculation formula and filling value calculation based on the MGNN (Mahalanobis-Gray and Nearest Neighbor algorithm) algorithm.The experimental results show that the filling accuracy of the method is higher than traditional KNN algorithm and MGNN algorithm.

Combination of K-Nearest Neighbor and K-Means based on Term Re-weighting for Classify Indonesian News

KNN is one of the accepted classification tool, it used all training samples in the classification which cause to a high level of computation complexity.To resolve this problem, it is necessary to combine traditional KNN algorithm and K-Means cluster algorithm that is proposed in this paper.After completing the preprocessing step, the first thing to do is weighting the word (term) by usingTerm Frequency-Inverse Document Frequency (TF-IDF). TF-IDF weightedthe words calculating the number of words that appear in a document. Second, grouping all the training samples of each category of K-means algorithm, and take all the cluster centers as the new training sample. Third, the modified training samples are used for classification with KNN algorithm. Finally, calculate the accuracy of the evaluation using precision, recall and f-measure. The simulation results show that the combination of the proposed algorithm in this study has a percentage accuracy reached 87%, an average value of f-measure evaluation= 0.8029 with the best k-values= 5 and the computation takes 55 second for one document. General Terms Data Mining, Text Classification, Clustering