Adaptive K-nearest Neighbor Research Articles

EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models.

lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More importantly, they were validated only under cross validation on lncRNA-protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptive k-nearest neighbor classifier and Deep models, this study focuses on systematically finding underlying linkages between lncRNAs and proteins. First, five LPI-related datasets are arranged. Second, multiple source features are integrated to depict an lncRNA-protein pair. Third, adaptive k-nearest neighbor classifier, deep neural network, and deep forest are designed to score unknown lncRNA-protein pairs, respectively. Finally, interaction probabilities from the three predictors are integrated based on a soft voting technique. In comparing to five classical LPI identification models (SFPEL, PMDKN, CatBoost, PLIPCOM, and LPI-SKF) under fivefold cross validations on lncRNAs, proteins, and LPIs, EnANNDeep computes the best average AUCs of 0.8660, 0.8775, and 0.9166, respectively, and the best average AUPRs of 0.8545, 0.8595, and 0.9054, respectively, indicating its superior LPI prediction ability. Case study analyses indicate that SNHG10 may have dense linkage with Q15717. In the ensemble framework, adaptive k-nearest neighbor classifier can separately pick the most appropriate k for each query lncRNA-protein pair. More importantly, deep models including deep neural network and deep forest can effectively learn the representative features of lncRNAs and proteins.

Maneuvering Multitargets Tracking System Using Surveillance Multisensors

Multisensors multitargets tracking is an important task, and the difficulty is how to form the real trajectory of each target accurately. In this article, we construct a complete system for tracking highly maneuvering multitargets in the case of multisensors asynchronous sampling. Our system divides the entire tracking task into three modules: space–time calibration, point-trajectory data association, and trajectory data fusion. An improved least-squares (LS) virtual fusion method is proposed to correct the asynchronous sampling time, and the fast vector calibrates the multisensors’ spatial state. In the point-trajectory data association module, an adaptive K-nearest neighbors (AK-NN) algorithm is proposed, which employs the adaptive threshold forming multiple trajectories. In trajectory data fusion, a CV+CT+S multimotion model is proposed with the multisensors’ probabilistic data association filter (PDAF) algorithm to track highly maneuvering multitargets actively. The results show that our system performs accurately for moving maneuvering multitargets tracking in complex situations.

An Improved K-Nearest Neighbor Algorithm for Pattern Classification

This paper proposed a “Locally Adaptive K-Nearest Neighbor (LAKNN) algorithm” for pattern exploration problem to enhance the obscenity of dimensionality. To compute neighborhood local linear discriminant analysis is an effective metric which determines the local decision boundaries from centroid information. KNN is a novel approach which uses in many classifications problem of data mining and machine learning. KNN uses class conditional probabilities for unfamiliar pattern. For limited training data in high dimensional feature space this hypothesis is unacceptable due to disfigurement of high dimensionality. To normalize the feature value of dissimilar metrics, Standard Euclidean Distance is used in KNN which s misguide to find a proper subset of nearest points of the pattern to be predicted. To overcome the effect of high dimensionality LANN uses a new variant of Standard Euclidian Distance Metric. A flexible metric is estimated for computing neighborhoods based on Chi-squared distance analysis. Chi-squared metric is used to ascertains most significant features in finding k-closet points of the training patterns. This paper also shows that LANN outperformed other four different models of KNN and other machine-learning algorithm in both training and accuracy.

Unsupervised Outlier Detection for Mixed-Valued Dataset Based on the Adaptive k-Nearest Neighbor Global Network

Outlier detection aims to reveal data patterns different from existing data. Benefit from its good robustness and interpretability, the outlier detection method for numerical dataset based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -Nearest Neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN) network has attracted much attention in recent years. However, the datasets produced in many practical contexts tend to contain both numerical and categorical attributes, that are, the datasets with mixed-valued attributes (DMAs). And, the selection of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is also an issue that is worthy of attention for unlabeled datasets. Therefore, an unsupervised outlier detection method for DMA based on an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN global network is proposed. First, an adaptive search algorithm for the appropriate value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> considering the distribution characteristics of datasets is introduced. Next, the distance between mixed-valued data objects is measured based on the Heterogeneous Euclidean-Overlap Metric, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN of a data object is obtained. Then, an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN global network is constructed based on the neighborhood relationships between data objects, and a customized random walk process is executed on it to detect outliers by using the transition probability to limit behaviors of the random walker. Finally, the effectiveness, accuracy, and applicability of the proposed method are demonstrated by a detailed experiment.

Adaptive Knn-based algorithm for network selection in next-generation networks

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.

Efficient Classification with Adaptive KNN

In this paper, we propose an adaptive kNN method for classification, in which different k are selected for different test samples. Our selection rule is easy to implement since it is completely adaptive and does not require any knowledge of the underlying distribution. The convergence rate of the risk of this classifier to the Bayes risk is shown to be minimax optimal for various settings. Moreover, under some special assumptions, the convergence rate is especially fast and does not decay with the increase of dimensionality.

Classification of Handheld Laser Scanning Tree Point Cloud Based on Different KNN Algorithms and Random Forest Algorithm

Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.

Minimax Rate Optimal Adaptive Nearest Neighbor Classification and Regression

k Nearest Neighbor (kNN) method is a simple and popular statistical method for classification and regression. For both classification and regression problems, existing works have shown that, if the distribution of the feature vector has bounded support and the probability density function is bounded away from zero in its support, the convergence rate of the standard kNN method, in which k is the same for all test samples, is minimax optimal. On the contrary, if the distribution has unbounded support, we show that there is a gap between the convergence rate achieved by the standard kNN method and the minimax bound. To close this gap, we propose an adaptive kNN method, in which different k is selected for different samples. Our selection rule does not require precise knowledge of the underlying distribution of features. The proposed adaptive method significantly outperforms the standard one. We characterize the convergence rate of the proposed adaptive method, and show that it matches the minimax lower bound.

A Novel Three-Step Classification Approach Based on Time-Dependent Spectral Features for Complex Power Quality Disturbances

Power quality events caused by renewable-energy integration are usually associated with complex disturbances; therefore, their type identification is the primary task of subsequent pollution control. This study proposes a novel three-step classification approach based on time-dependent spectral features (TDSFs) for the classification of complex power quality disturbances (PQDs). First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is adopted to decompose the PQDs into several intrinsic mode functions (IMFs). The related IMFs are selected by correlation coefficient and kurtosis. Then the eight eigenvalues of each IMF are extracted, including TDSFs. Finally, the eigenvalue dimension of each IMF is reduced by linear discriminant analysis (LDA). Moreover, the classifier of the adaptive k-nearest neighbor with excluding outliers (AdaKNNEO) can confirm the PQDs type. To verify the effectiveness of the proposed approach, a series of simulations and hardware experiments are conducted. The overall result shows robustness and high accuracy of the proposed method, and especially for the complex PQDs, it possesses the highest entirely correct of 96% compared to other advanced methods.

Fingerprint-Based Localization Performance Analysis: From the Perspectives of Signal Measurement and Positioning Algorithm

This article analyzes the localization performance of fingerprinting positioning system from the perspectives of the signal measurement and positioning algorithm. Unlike the existing works which have not taken the influence of grid size into account, first of all, we construct a novel derivation model involving the grid size information. Then, from the signal measurement's perspective, the localization performance is analyzed based on our new model under two cases: with specific and nonspecific signal distributions. For the first case, we utilize the traditional knowledge of Cramér-Rao lower bound (CRLB) to rededuce it. For the second case, a Gaussian-Markov theorem method is introduced to conduct derivation. Furthermore, from the latter perspective, we first analyze the localization performance of the mostly used k-nearest neighbors (KNN) algorithm leveraging the probability density function (PDF) of these nearest neighbors. Then, a novel adaptive KNN algorithm is designed based on the derivation result, which has improved the location accuracy by about 20%. Finally, extensive simulations and real experiments are conducted to show the effectiveness of our claims.

Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection

Multi-view feature selection aims at obtaining a subset of informative features from heterogeneous feature domains. Recent graph based approaches mostly learn view-specific feature selection matrices by virtue of prepared single-view graphs, and weight the view-wise objectives to discriminate them. However, the majority of them encounter that (i) the dimensions of vectors for evaluating features in different views are inconsistent and (ii) the similarities attained by using features in the original high-dimensional space are inferior. As a result, the joint evaluation of heterogeneous features using view-specific selection matrices are inaccurate and immensely depend on ill-defined similarity relations. To overcome them, we propose the Multilevel projections with Adaptive neighbor graph model for unsupervised Multi-view Feature Selection (MAMFS). Our formulation learns the collaborative graph adapting to the adaptive k-nearest neighbors in subspaces, wherein the multilevel projections are involved including the weighted view-specific projections that discriminate different views and the joint projection that collaborates different views. Benefited from this, both of view-within information and the view-across information are jointly explored and made use of. Extensive simulations are conducted to validate the superiority of the proposed method compared to the state-of-the-art competitors.

A new globally adaptive k-nearest neighbor classifier based on local mean optimization

The k-nearest neighbor (KNN) rule is a simple and effective nonparametric classification algorithm in pattern classification. However, it suffers from several problems such as sensitivity to outliers and inaccurate classification decision rule. Thus, a local mean-based k-nearest neighbor classifier (LMKNN) was proposed to address these problems, which assigns the query sample with a class label based on the closest local mean vector among all classes. It is proven that the LMKNN classifier achieves better classification performance and is more robust to outliers than the classical KNN classifier. Nonetheless, the unreliable nearest neighbor selection rule and single local mean vector strategy in LMKNN classifier severely have negative effect on its classification performance. Considering these problems in LMKNN, we propose a globally adaptive k-nearest neighbor classifier based on local mean optimization, which utilizes the globally adaptive nearest neighbor selection strategy and the implementation of local mean optimization to obtain more convincing and reliable local mean vectors. The corresponding experimental results conducted on twenty real-world datasets demonstrated that the proposed classifier achieves better classification performance and is less sensitive to the neighborhood size $$k$$ compared with other improved KNN-based classification methods.

MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier

Brain tumor characterizes the aggregation of abnormal cells in specific tissues of the brain zone. The prior distinguishing proof of brain tumors has a huge influence on the treatment and recovery of the patient. The identification of a brain tumor and its evaluation is commonly a troublesome and tedious assignment. For effective classification and grading of brain tumor images, in this paper, we present an automatic MRI brain tumor classification system. The proposed work consists of four modules namely, pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in the input image is removed using the Median Filter because the noises present in the input images will affect the accuracy of the classification process. At once, the images are converted into 3 × 3 blocks. Then, the texture features are extracted from the pre-processed image. After the feature extraction process, the features are given to the adaptive k-nearest neighbor classifier to classify an image as normal or abnormal. Later, the tumor regions are segmented with the help of the optimal possibilistic fuzzy C-means clustering algorithm. Both classification and the segmentation appearance technique are evaluated in terms of accuracy; sensitivity as well as specificity. For experimental analysis two dataset are utilized namely, BRATS MICCAI brain tumor dataset and publically available dataset.

A new locally adaptive [formula omitted]-nearest neighbor algorithm based on discrimination class

The k-nearest neighbor (kNN) rule is a classical non-parametric classification algorithm in pattern recognition, and has been widely used in many fields due to its simplicity, effectiveness and intuitiveness. However, the classification performance of the kNN algorithm suffers from the choice of a fixed and single value of k for all queries in the search stage and the use of simple majority voting rule in the decision stage.In this paper, we propose a new kNN-based algorithm, called locally adaptive k-nearest neighbor algorithm based on discrimination class (DC-LAKNN). In our method, the role of the second majority class in classification is for the first time considered. Firstly, the discrimination classes at different values of k are selected from the majority class and the second majority class in the k-neighborhood of the query. Then, the adaptive k value and the final classification result are obtained according to the quantity and distribution information on the neighbors in the discrimination classes at each value of k.Extensive experiments on eighteen real-world datasets from UCI (University of California, Irvine) Machine Learning Repository and KEEL (Knowledge Extraction based on Evolutionary Learning) Repository show that the DC-LAKNN algorithm achieves better classification performance compared to standard kNN algorithm and nine other state-of-the-art kNN-based algorithms.

Machine learning method for cosmetic product recognition: a visual searching approach

A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the ‘Kaggle’ website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

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.

Image interpolation with adaptive k ‐nearest neighbours search and random non‐linear regression

Learning-based image interpolation methods have been proved to be effective in image interpolation. In this study, the authors propose an accurate image interpolation with adaptive k-nearest neighbour searching and non-linear regression. The proposed method aims to find k-nearest neighbours of the input image patch and use them to learn the non-linear mapping between low-resolution and high-resolution image patches. To be specific, they first divide the training image patches into many subspaces, then they utilise an adaptive robust and precise k nearest neighbour searching scheme with proposed normalised Gaussian similarity to find the k nearest neighbours in the matched subspace. The selected k image patch pairs are then used to learn the non-linear regression model through an extreme learning machine. Furthermore, the proposed interpolation method is a cascade framework that consists of two stages. Stage 2 takes the results of Stage 1 as input to further improve the performance. Extensive experimental results on commonly used test images and image datasets indicate that their proposed algorithm obtains competitive performance against the state-of-the-art methods both in terms of objective evaluation values and the subjective effect of reconstructed images.

A Non-intrusive home load identification method based on adaptive reinforcement learning algorithm

At present, the load data collection of residential users mainly starts from the lower acquisition frequency, so the non-intrusive home load identification method based on low frequency sampling has attracted wide attention. However, low-frequency sampling has a low recognition accuracy when the training data set is small. Therefore, a non-intrusive home load identification method based on adaptive KNN reinforcement learning algorithm is proposed. The method firstly analyzes the state of the electrical appliance by KNN to obtain the initial HMM model, and then solves the HMM model by adaptive KNN reinforcement learning algorithm to obtain the optimal state transition strategy. This method reduces the model pair data. The dependence improves the recognition accuracy of the model and the adaptability to new data. Finally, the experimental verification is carried out by the low frequency data set AMPds. The results show that the method improves the state recognition accuracy of the electrical appliance and enhances the adaptability of the algorithm to new data.

Multi-Model- and Soft-Transition-Based Height Soft Sensor for an Air Cushion Furnace.

The floating height of the strip in an air cushion furnace is a key parameter for the quality and efficiency of production. However, the high temperature and high pressure of the working environment prevents the floating height from being directly measured. Furthermore, the strip has multiple floating states in the whole operation process. It is thus difficult to employ a single model to accurately describe the floating height in different states. This paper presents a multi-model soft sensor to estimate the height based on state identification and the soft transition. First, floating states were divided using a partition method that combined adaptive k-nearest neighbors and principal component analysis theories. Based on the identified results, a hybrid model for the stable state, involving a double-random forest model for the vibration state and a soft-transition model, was created to predict the strip floating height. In the hybrid model for the stable state, a mechanistic model combined thick jet theory and the equilibrium equation of force to cope with the lower floating height. In addition, a novel soft-transition model based on data gravitation that further reflects the intrinsic process characteristic was developed for the transition state. The effectiveness of the proposed approach was validated using a self-developed air cushion furnace experimental platform. This study has important value for the process prediction and control of air cushion furnaces.

Effective user preference mining-based personalised movie recommendation system

One of the primary issues of many websites is the suggestion of multiple choices to the users at the same time, which makes the task more complex and time consuming to find the right product. Web mining and recommendation system based on user behaviour helps users by providing essential information without asking explicitly. Several movie recommendation systems are available to suggest movies, but often they do not do that effectively. To achieve enhanced effectiveness and efficiency, user's movie ratings were retrieved, cleaned, formatted and grouped into proper, meaningful session and data profile was developed. In this paper, we have developed a new ontology for clear and better understanding of the movie domain. The user data consisting of movie ratings is used to recommend movies for the users. For the classification of users, we use adaptive K-nearest neighbour (AKNN) approach and post classification process; movies are recommended to the active target user. The obtained results of the proposed recommendation approach are compared with existing baseline methods, and the results prove that the presented approach to be proficient.