Traditional K-nearest Neighbor Algorithm Research Articles

A Multi-functional Electronic Glove for Multidimensional Environmental Perception and Gesture Recognition

In recent years, wearable electronic gloves with rich sensing functions have garnered widespread attention. Researchers are exploring various aspects of wearable devices including structural design, material innovation, and algorithm optimization. An analysis of demand suggests that wearable electronic gloves should possess sensory functions like hand, which includes roughness of contacted objects, assessing temperature of touched objects, determining grasp force, and interpreting gesture language. This paper presents a multi-sensing wearable flexible electronic glove equipped with graphene foam material sensors that can perceive external pressure, roughness, temperature, and express gesture language. The experimental results show that this glove exhibits excellent approximately linear-range sensing characteristics for force perception (1.475 kPa-1 at 0-0.4 kPa), outstanding temperature sensing capability (temperature sensitivity is -1.22% within the range of 30-80℃), and recognition judgment of 12 different gestures. Additionally, an optimized KNN (K-nearest neighbor) algorithm based on time-domain window is proposed. Compared with traditional KNN algorithm, KNN based on time-domain window improves the accuracy of gesture intent recognition from 86.15% to 91.93%. Wearable electronic gloves represent promising developments for fields such as hand rehabilitation training for patients, intention expression for individuals suffering from language barriers, and wearable multifunctional sensing applications.

A Novel Piano Arrangement Timbre Intelligent Recognition System Using Multilabel Classification Technology and KNN Algorithm.

In this paper, melody and harmony are regarded as the task of machine learning, and a piano arranger timbre recognition system based on AI (Artificial Intelligence) is constructed by training a series of samples. The short-time Fourier transform spectrum analysis method is used to extract the piano timbre characteristic matrix, and the electronic synthesis of timbre recognition is improved by extracting the envelope function. Using the traditional multilabel classification method and KNN (K-nearest neighbor) algorithm, a combined algorithm of these two algorithms is proposed. The experimental results show that the detection rate increases from 61.3% to 70.2% after using the combined classification algorithm. The correct rate also increased from 40.3% to 48.9%, and the detection rate increased to 74.6% when the K value was set to 6. The experimental results show that, compared with the traditional classification algorithm, this algorithm has a certain improvement in recognition rate. Using this system to recognize the timbre of piano arrangement has a high recognition accuracy, which is worthy of further popularization and application.

Research on Image Classification Algorithm Based on Convolutional Neural Network-TensorFlow

Image Classification is one of the important processing methods in the field of image processing. Traditional Classification algorithms, such as KNN (K-NearestNeighbor), have been unable to meet the accuracy standards of image Classification. And convolutional neural networks (CNN) are increasingly used to solve the problem of image classification. This paper briefly ces the convolutional neural network, which is constructed using the TensorFlow deep learning framework, and conducts experiments on 2000 pieces of custom data set of 10 classification obtained by crawler technology. At the same time, a comparative experiment is conducted on the traditional image classification KNN algorithm. Experimental results show that the application of convintroduolutional neural network in image classification has relatively large advantages, and the accuracy has been greatly improved.

A dynamic ensemble outlier detection model based on an adaptive k-nearest neighbor rule

Ensembles of outlier detectors are drawing increasing attentions recently, in spite of the difficulty on developing ensembles in the framework of unsupervised learning. We have noted that existing outlier ensembles often use certain fusion rules (e.g. majority voting) to aggregate individual learners. Theoretically, these individuals are assumed to be error-independent so that single models can be outperformed by the ensemble. However, it is of great difficulty to satisfy this assumption in practical applications. By dynamic selecting more competent individual(s) for each test pattern, this problem can be alleviated effectively. Inspired by this idea, this paper proposes a dynamic ensemble outlier detection model using one-class classifiers as base learners. As the competences of base detectors are estimated totally on data points in the validation set, its impact on the selection is significant. In order to achieve an efficient selection, we propose an adaptive k-nearest neighbor (KNN) rule, instead of traditional KNN algorithm, to constitute the validation set for each test pattern. Our adaptive KNN rule firstly uses algorithm support vector data description (SVDD) to mine the local area where class conditional probabilities are not constant in terms of the corresponding test pattern. Competences estimated with neighbor patterns in this area should thus be more accurate than that by traditional KNN rule. A probabilistic model that uses posterior probabilities of one-class classifiers is used then to estimate classifier competences. We present experimental evidence of the detection performance improvement over single models and over a variety of static ensemble models, by using data sets from UCI repository.

Medical Health Big Data Classification Based on KNN Classification Algorithm

The rapid development of information technology has led to the development of medical informatization in the direction of intelligence. Medical health big data provides a basic data resource guarantee for medical service intelligence and smart healthcare. The classification of medical health big data is of great significance for the intelligentization of medical information. Due to the simplicity of KNN (K-Nearest Neighbor) classification algorithm, it has been widely used in many fields. However, when the sample size is large and the feature attributes are large, the efficiency of the KNN algorithm classification will be greatly reduced. This paper proposes an improved KNN algorithm and compares it with the traditional KNN algorithm. The classification is performed in the query instance neighborhood of the conventional KNN classifier, and weights are assigned to each class. The algorithm considers the class distribution around the query instance to ensure that the assigned weight does not adversely affect the outliers. Aiming at the shortcomings of traditional KNN algorithm in processing large data sets, this paper proposes an improved KNN algorithm based on cluster denoising and density cropping. The algorithm performs denoising processing by clustering, and improves the classification efficiency of KNN algorithm by speeding up the search speed of K-nearest neighbors, while maintaining the classification accuracy of KNN algorithm. The experimental results show that the proposed algorithm can effectively improve the classification efficiency of KNN algorithm in processing large data sets, and maintain the classification accuracy of KNN algorithm well, and has good classification performance.

A Time-Efficient Approach for Decision-Making Style Recognition in Lane-Changing Behavior

Fast recognition of a driver's decision-making style when changing lanes plays a pivotal role in a safety-oriented and personalized vehicle control system design. This article presents a time-efficient recognition method by integrating k-means clustering (k-MC) with the K-nearest neighbor (KNN) algorithm, called kMC-KNN. Mathematical morphology is implemented to automatically label the decision-making data into three styles (moderate, vague, and aggressive), while the integration of k-MC and the KNN algorithm helps to improve the recognition speed and accuracy. Our developed mathematical-morphology-based clustering algorithm is then validated by a comparison with agglomerative hierarchical clustering. Experimental results demonstrate that the developed kMC-KNN method, in comparison with the traditional KNN algorithm, can shorten the recognition time by more than 72.67% with a recognition accuracy of 90-98%. In addition, our developed kMCKNN method also outperforms a support vector machine in terms of recognition accuracy and stability. The developed time-efficient recognition approach would have great application potential for in-vehicle embedded solutions with restricted design specifications.

Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

To improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

Modification of KNN Algorithm

K-Nearest Neighbor (KNN) classification is one of the most fundamental and simple classification methods. It is among the most frequently used classification algorithm in the case when there is little or no prior knowledge about the distribution of the data. In this paper a modification is taken to improve the performance of KNN. The main idea of KNN is to use a set of robust neighbors in the training data. This modified KNN proposed in this paper is better from traditional KNN in both terms: robustness and performance. Inspired from the traditional KNN algorithm, the main idea is to classify an input query according to the most frequent tag in set of neighbor tags with the say of the tag closest to the new tuple being the highest. Proposed Modified KNN can be considered a kind of weighted KNN so that the query label is approximated by weighting the neighbors of the query. The procedure computes the frequencies of the same labeled neighbors to the total number of neighbors with value associated with each label multiplied by a factor which is inversely proportional to the distance between new tuple and neighbours. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the significant improvement in the performance of KNN method.

Short-Term Traffic Volume Forecasting with Asymmetric Loss Based on Enhanced KNN Method

Short-term traffic volume forecasting is one of the most essential elements in Intelligent Transportation System (ITS) by providing prediction of traffic condition for traffic management and control applications. Among previous substantial forecasting approaches, K nearest neighbor (KNN) is a nonparametric and data-driven method popular for conciseness, interpretability, and real-time performance. However, in previous related researches, the limitations of Euclidean distance and forecasting with asymmetric loss have rarely been focused on. This research aims to fill up these gaps. This paper reconstructs Euclidean distance to overcome its limitation and proposes a KNN forecasting algorithm with asymmetric loss. Correspondingly, an asymmetric loss index, Imbalanced Mean Squared Error (IMSE), has also been proposed to test the effectiveness of newly designed algorithm. Moreover, the effect of Loess technique and suitable parameter value of dynamic KNN method have also been tested. In contrast to the traditional KNN algorithm, the proposed algorithm reduces the IMSE index by more than 10%, which shows its effectiveness when the cost of forecasting residual direction is notably different. This research expands the applicability of KNN method in short-term traffic volume forecasting and provides an available approach to forecast with asymmetric loss.

MIME-KNN: Improve KNN Classifier Performance Include Classification Accuracy and Time Consumption

The K Nearest Neighbor (KNN) classifier has been widely used in the applications of data mining and machine learning, because of its simple implementation and distinguished performance. However, because the distance between all training samples and test samples have to be calculated, when there are too many samples or samples have huge features dimensionality, the time complexity and space complexity are high. The paper proposes a KNN algorithm with the minimum intra-class distance and the maximum extra-class distance (MIME-KNN). By finding a transformation matrix, the algorithm minimizes the intra-class distance and maximizes the distance between classes, which can improve the classification performance of traditional KNN algorithm. At the same time, the algorithm will also reduce the dimensionality of the samples to achieve the purpose of reducing time and space complexity. Experimental results show that the MIME-KNN work well in practical.

Resolution Recommendation for Event Tickets in Service Management

In recent years, IT service providers have rapidly achieved an automated service delivery model. Software monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

Context-Aware Personalization Using Neighborhood-Based Context Similarity

With the overwhelming volume of online multimedia content and increasing ubiquity of Internet-enabled mobile devices, pervasive use of the Web for content sharing and consumption has become our everyday routines. Consequently, people seeking online access to content of interest are becoming more and more frustrated. Thus, deciding which content to consume among the deluge of available alternatives becomes increasingly difficult. Context-aware personalization, having the capability to predict user's contextual preferences, has been proposed as an effective solution. However, some existing personalized systems, especially those based on collaborative filtering, rely on rating information explicitly obtained from users in consumption contexts. Therefore, these systems suffer from the so-called cold-start problem that occurs as a result of personalization systems' lack of adequate knowledge of either a new user's preferences or of a new item rating information. This happens because these new items and users have not received or provided adequate rating information respectively. In this paper, we present an analysis and design of a context-aware personalized system capable of minimizing new user cold-start problem in a mobile multimedia consumption scenario. The article emphasizes the importance of similarity between contexts of consumption based on the traditional k-nearest neighbor algorithm using Pearson Correlation model. Experimental validation, with respect to quality of personalized recommendations and user satisfaction in both contextual and non-contextual scenarios, shows that the proposed system can mitigate the effect of user-based cold-start problem.

An Improved Algorithm of Speech Emotion Recognition

The traditional K-nearest neighbor algorithm existences of the risk of miscarriage of justice, for its shortage a speech emotion recognition algorithm based on fuzzy K-nearest neighbors is proposed. By introducing the fuzzy membership concept, different characteristic parameters for the different contribution of emotion recognition are calculated, and the weighted Euclidean distance is used in speech emotion recognition. The experimental results show the effectiveness of the algorithm.

Classification Algorithm Based on Natural Nearest Neighbor

K-nearest Neighbor (KNN) is a widely used classiflcation method that operates by a majority vote on a k{ nearest neighbor set. But when the KNN is used to deal with the datasets with difierent characteristics in classiflcation, it is di‐cult to select an appropriate parameter k which afiects obviously the performance and e‐ciency of the algorithm. Therefore, how to select appropriate parameter k value of KNN algorithm has been an open issue in the fleld of data mining. So, Natural Nearest Neighbor (3N) proposed by us is a novel concept on nearest neighbor, which does not need a parameter K and in which the neighbors of each point are obtained by an adaptive algorithm. This paper proposes a Classiflcation Algorithm based on Natural Nearest Neighbor (CAb3N). Comprehensive experimental results on the UCI dataset conflrm the claims that CAb3N not only has the advantage of parameter-free, but also has the better accuracy and overall performance both than the traditional KNN algorithm and hubness-based weighted KNN classiflcation algorithm.

A new fast reduction technique based on binary nearest neighbor tree

The K-nearest neighbor ( KNN ) rule is one of the most useful supervised classification methods, and is widely used in many pattern classification applications due to its simplicity. However, it faces prohibitive computational and storage requirements when dealing with large datasets. A reasonable way of alleviating this problem is to extract a small representative subset from the original dataset without reducing the classification accuracy. This means the most internal patterns are removed and the boundary patterns that can contribute to better classification accuracy are retained. To achieve this purpose, a new algorithm based on binary tree technique and some reduction operations is presented. The key issues of the proposed algorithm are how to build binary nearest neighbor search tree and design reduction strategies to keep the high classification accuracy patterns. In particular, firstly, we utilize several tree control rules and KNN rule to build a binary nearest neighbor tree of each random pattern. Secondly, according to the node locations in each binary nearest neighbor tree and the strategies of selection and replacement, different kinds of patterns as prototypes are obtained, which are close to class boundary regions or locate in the interior regions, and some internal patterns are generated. Finally, experimental results show that the proposed algorithm effectively reduces the number of prototypes while maintaining the same level of classification accuracy as the traditional KNN algorithm and other prototype algorithms. Moreover, it is a simple and fast hybrid algorithm for prototype reduction.

A Novel K-Nearest Neighbor Algorithm Based on I-Divergence with Application to Soil Moisture Estimation in Maize Field

This paper proposes a novel k-nearest neighbor algorithm to predict soil moisture in maize field. In order to estimate soil moisture in maize field accurately without any destruction to root and soil, this paper uses biological characteristics of maize to estimate soil moisture, including plant height, leaf area, stem diameter, dry weight and fresh weight, all the values of which are non-negative. So a novel k-nearest neighbor based on I-divergence (ID_KNN) is proposed. ID_KNN uses I-divergence as the distance metric instead of Euclidean distance, which is more effective when the data is positive. The proposed method is tested on datasets in six growth stages of maize, and the experimental results show that ID_KNN is more effective in accuracy and macro F1 measure than traditional k-nearest neighbor algorithm.

Short-term Traffic Flow Forecasting based on Two-tier K-nearest Neighbor Algorithm

The K-nearest Neighbor algorithm does not require a priori knowledge and its forecasting results are better than those of the linear model algorithm. However, its computing speed is low and its parameter adjustment method is not flexible enough. Based on the traditional K-nearest neighbor algorithm, this paper proposes a two-tier K-nearest neighbor algorithm. Combined with the actual traffic flow, it calibrates the algorithm parameter to improve the calculation speed and the accuracy of the algorithm.

The Spatial Classification Algorithm of K-Nearest Neighbor Based on Spatial Predicate

Traditional k-Nearest Neighbor Algorithm (short for KNN) is usually used in the spatial classification; however, the problem of low-speed searching exists in this method. In order to avoid this kind of disadvantage, this paper puts forward a new spatial classification algorithm of K-nearest neighbor based on spatial predicate. This method searches the object set which is similar to the test object in spatial concept and uses spatial predicate to help search the object set, which narrows the searching range and reduces the operating time of KNN algorithm.