Improving the quality of K-NN graphs through vector sparsification: application to image databases

Aim: Coronary illness is one of the main sources of mortality in this current world. A basic test in the field of clinical information assessment is the expectation of cardiovascular infection. The fundamental target of this paper is to work on the exactness of the Data mining algorithms being used to improve heart disease prediction. We will be comparing the novel Random forest with K Nearest Neighbor to find out which of these can give us the best accuracy. Material and Methods: The study used 98 samples with Novel Random Forest and K Nearest Neighbor is executed with varying training and testing splits for predicting the accuracy for heart disease prediction with the 80% of G-power value and data about cardiovascular disease was gathered from numerous web sources, including latest research findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the accuracy rate of the classifiers is examined using the heart disease dataset.. There was a statistical insignificant difference among Innovative Random Forest and K Nearest Neighbor p = 0.484 (p>0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and K Nearest Neighbor 67.21% is obtained. Conclusion: Prediction of Heart disease using the innovative Random Forest (RF) technique appears to be a significant improvement over the K Nearest Neighbor (KNN) algorithm in terms of accuracy.

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods: Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion: From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors

<p>Satellite based rainfall estimates (SBRE) are used as an alternative to gauge rainfall in hydrological studies particularly for basins with data issues. However, these data products exhibit errors which cannot be always corrected by bias correction methods such as Ratio Bias Correction (RBC). Data fusion or data merging can be a potentially good approach in merging various SBREs to obtain a fused dataset, which can benefit from all the data sources and may minimise the error in rainfall estimates. Data merging methods which are commonly applied in meteorology and hydrology are: Arithmetic merging method (AMM), Inverse error squared weighting (IESW) and Error variance (EV). Among these methods EV is popular, which merges bias corrected SBREs using the minimisation of variance principle.</p><p>In this research we propose using K Nearest Neighbour (KNN) as a data merging method. KNN has a particular advantage as it does not depend upon any specific statistical model to merge data and presents a great flexibility as the value of K (the number of neighbours to be chosen) can be varied to suit the purpose (for example, choosing different K values for different seasons). In this research it is proposed to compute the distances of bias corrected SBREs of the training data from the gauge data and to assign the SBRE with the minimum distance as the class C where C = 1, 2, 3,…, number of SBREs. In validation each data point consisting of a value of each SBRE may be compared with the data points from the training set and the class of the data point(s) closest to this data point is assigned as the class of the validation data point.</p><p>The KNN approach as a data merging method was applied to the Indus basin in Pakistan. Three satellite rainfall products CMORPH, PERSIANN CDR and TRMM 3B42 with 0.25° x 0.25° spatial and daily temporal resolution were used. Based on the climatic and physiographic features the Indus basin was divided into four zones. Rainfall products were compared at daily, weekly, fortnightly, monthly and seasonally whereas spatial scales were gauge location, zonal scales and basin scale. The RBC method was used to correct the bias. The KNN method with K=1, 3 and 5 was used and compared with other merging methods namely AMM, IESW and EV. The results were compared in two seasons i.e. non-wet and wet season. AMM and EV methods performed similarly whereas IESW performed poorly at zonal scales. KNN merging method outperformed all other merging methods and gave lowest error across the basin. The daily normalised root mean square error at the Indus basin scale was reduced to 0.3, 0.45 and 0.45 respectively with KNN, AMM and EV whereas this error was 0.8, 0.65 and 0.53 respectively in CMORPH, PERSIANN CDR and TRMM datasets. The KNN merged product gave lowest error at daily scale in calibration and validation period which justifies that merging with KNN improves rainfall estimates in sparsely gauged basins.</p><p> </p><p><strong>Key words:</strong> Merging, data fusion, K nearest neighbour, KNN, error variance, Indus.</p>

Traditional k-nearest neighbor methods couldn’t be able to correctly classify objects when their k nearest neighbors are dominated by other classes. This paper formulates a two-class classification problem, and applies a modified k-nearest neighbors (KNN) classifier algorithm based on maximal coherence, validity ratings, and k-fold cross validation to classify the test samples. We build a validity score for the pairs of sample and their surroundings according to their labels. The k nearest neighbors (including the unknown test object) of each sample in the training set as well as the unknown test object itself will be determined. The unknown test object will be tentatively assigned to a class membership. Then we use the validity scores to quantify the degree to which a pre-determined group of samples resemble their k nearest neighbors. A classifier is designed which take into account the coherence and validity ratings. A numerical example demonstrates the effectiveness of the algorithm in detail. The enhanced KNN method is compared with the conventional KNN and the modified KNN method on both real world wine data and photo-thermal infrared imaging spectroscopy (PT-IRIS) data for up to 20 different k values. Classification accuracy of KNN method and our method in terms of various combinations of k-value and k-fold cross validation are compared. The experimental results show that the proposed enhanced KNN method outperforms the conventional KNN and the modified KNN method on both real world wine data and PT-IRIS data. In addition, the classification accuracy of both the conventional KNN and our method increase drastically when k = 5. The average classification accuracy of our method on the PT-IRIS data featuring small sample size and high overlap is 97.87%.

The k Nearest Neighbors (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we propose a novel deep learning architecture, which is called the Deep Similarity-Enhanced K Nearest Neighbors (DSE-KNN), to learn an optimized similarity function of the data directly towards the goal of optimizing the KNN decision making. In other words, the type of similarity function that is used in our method is not pre-determined but rather learned to map data to a high-dimensional feature space where the accuracy of the KNN decision making is maximized. Experimental results show that DSE-KNN outperforms other common machine learning methods on classifying different types of disease datasets and predicting daily price direction of different stock ETFs.

Hotel adalah bentuk akomodasi yang dikelola secara komersial, disediakan bagi semua orang untuk mendapatkan layanan dan penginapan termasuk untuk makan dan minum. Tujuan pembuatan aplikasi ini adalah untuk memudahkan para konsumen guna mendapatkan/memesan hotel secara mudah dengan menggunakan aplikasi. Sistem pendukung keputusan dapat merekomendasikan kepada calon pelanggan untuk menentukan hotel pilihan secara cepat, tepat, dan akurat. Dalam aplikasi ini, dirancang sistem berbasis web yang menggunakan bahasa pemograman PHP berdasarkan database MariaDB. Aplikasi ini juga hanya melakukan pencarian hotel di Depok dan mencari hotel terdekat berdasarkan jarak, harga, dan rating berdasarkan tempat kita berada, yaitu di daerah Kota Depok. Sistem dikembangkan dengan menggunakan algoritma KNearest Neighbor (K-NN). Metode ini dipilih karena metode K- Nearest Neighbor adalah bentuk model pendukung keputusan yang dapat mengklasifikasikan data berdasarkan jarak terdekat. K-Nearest Neighbor adalah formula paling sederhana yang sering digunakan untuk mengimplementasikan pencarian jarak jauh. Hasil penelitian menunjukan bahwa Metode K-NN (K-Nearest Neighbor) dapat diterapkan dengan rumus Euclidean Distance untuk perancangan aplikasi sistem pencarian hotel yang menggunakan K- Nearest Neighbor dengan menerapkan kriteria seperti alamat dari pelanggan, harga, dan peringkat yang ditentukan sehingga dapat diimplementasikan sebagai media rekomendasi untuk pencarian hotel. Semakin banyak data training hotel yang di-input dan digunakan, tingkat keakuratan akan semakin baik dan tepat.
 
 Kata Kunci: perancangan aplikasi, K-Nearest Neighbor, pemesanan hotel

A very important part of machine learning is classification. One of the most important classification algorithms is the k-nearest neighbour algorithm. This algorithm works on the principle that a point belongs to the same class as the majority of its closest neighbours. In this approach, a point qualifies as one of the closest neighbours of another point based on the parameter ‘k’. We determine whether a point has a bearing on another neighbour’s class if it is one of the first ‘k’-nearest neighbours of the point in question. A major disadvantage of this algorithm is the fact that we are bound to take exactly ‘k’-nearest neighbours irrespective of the distance between a point and the neighbour. This is a major anomaly. We decide that we shall determine the importance of a point in an algorithm based on proximity to another point but do not pay any heed to the distance between those two points. Many solutions have been proposed to reduce the effect of this problem in the algorithm. This paper gets rid of this problem altogether and adds another dimension to the nearest neighbour classification model.

Missing data is a common problem in many domains that rely on data analysis. The k Nearest Neighbors imputation method has been widely used to address this issue, but it has limitations in accurately imputing missing values, especially for datasets with small pairwise correlations and small values of k. In this study, we proposed a method, Ranked k Nearest Neighbors imputation that uses a similar approach to k Nearest Neighbor, but utilizing the concept of Ranked set sampling to select the most relevant neighbors for imputation. Our results show that the proposed method outperforms the standard k nearest neighbor method in terms of imputation accuracy both in case of Missing Completely at Random and Missing at Random mechanism, as demonstrated by consistently lower MSIE and MAIE values across all datasets. This suggests that the proposed method is a promising alternative for imputing missing values in datasets with small pairwise correlations and small values of k. Thus, the proposed Ranked k Nearest Neighbor method has important implications for data imputation in various domains and can contribute to the development of more efficient and accurate imputation methods without adding any computational complexity to an algorithm.

Prostate cancer is one of the most common cancers in men, and the cases of this disease is increasing. Histopathological examination of prostate cancer is one of the main procedures for prostate cancer detection. The structural changes of the cytoplasm, stroma, lumen and nucleus in the glandular tissue will indicate the presence of cancerous or non-cancerous areas in the histopathology of prostate cancer. Therefore, a framework was developed to automatically segment and classify glandular tissue into cytoplasm, stroma, lumen, and nucleus, which can reduce the complexity of prostate cancer detection. The images underwent image enhancement using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, in segmentation phase, K-means clustering (KMC) and multi-level thresholding (MT) methods were implemented to segment the enhanced image into cytoplasm and stroma, lumen and nuclei regions. A total of 8 feature vectors are extracted from each segmented image. All these features were introduced into the classification system namely K nearest neighbor (KNN) and decision tree (DT). The overall results showed that the performance of KNN is better than DT with an accuracy of 86.67%, sensitivity and specificity are both 100% (the features of the KMC category). With the features of MT category, KNN achieved 84.44% in term of accuracy, 100% sensitivity and 96.67% specificity. Here, it can also be concluded that the features of the KMC category are more suitable for the classifiers. In addition, leave-one-out cross-validation has been implemented, which can improve the performance of the two classifiers.

In this paper, we present a method for detection of ischemia by using heart rate variability (HRV) during supine-standing and controlled breathing with 12 breaths/min and 6 breaths/min. The feature vector was extracted from the energy of HRV at different frequency bands using adaptive optimal kernel time-frequency representation of HRV and the K nearest neighborhood (KNN) classifier was utilized to classify the HRV data obtained from 101 subjects. By using a modular classifier based on KNN, a sensitivity/specificity of 78.6/100.0% and an accuracy of 91.4% were achieved.KeywordsHeart Rate VariabilityDeep BreathAutonomic TestControl BreathingHeart Rate Variability DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In order to solve the problem of ignoring the correlation between class labels, this paper describes a new method for multi-label classification based on the frequent item sets to classify an unseen instance on the basis of its k nearest neighbors( MLFI-KNN). For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach as compared to some existing well-known methods.

The KNN (The K nearest neighbor) is known as its simple efficient and widely used in classification problems or as a benchmark in classification problems. For different data types especially complex structure and high-dimensional data in real-life, the choice of distance metrics between sample points is a relatively complexity problem. The KNN's feature space is generally n-dimensional real vector space. This article converts the samples in the vector space to be the elements in line with the Lie group nature and then proposes a Li-KNN algorithm to solve the classification problem based on the theory of Lie groups. It shows good results by the experimental on handwritten numeral.

Autonomous navigation is an important feature that allows the robot to move independently from a point to another without a tele-operator. This feature makes mobile robots useful in many tasks that require transportation, exploration, surveillance, guidance, inspection …etc. Furthermore, autonomous robots deal with real time environments that tend to be complex, nonlinear and partially observed. They also operate with limited memory resources and tight time constraints. In this paper, we present an investigation related to mobile robot navigation. We first compare a group of classification algorithms using real traces of wall following robot navigation. Then we focus on the k Nearest Neighbors (KNN) algorithm to improve it and help it be more applicable in autonomous robot navigation. We applied a Nearest Neighbor set reduction technique to help reduce the high running time of KNN. The results indicate that KNN is a competing algorithm especially after decreasing the running time significantly by a factor of 19 and combining that with the KNN’s existing features. Results are further improved by applying an attribute selection method.

Previous studies have achieved accurate predictions for Marshall design parameters (MDPs), but their limited data and input variables might restrict generalization. In this study, machine learning (ML) was used to predict MDPs with more generalised models. To achieve this, a dataset was collected from six different papers. Inputs were material properties and their ratios in the mixture, while target features were six MDPs used in mixture design. Four ML algorithms were used including linear regression, polynomial regression, k nearest neighbour (KNN) and support vector regression (SVR). Also, the cross-validation (CV) method was used to detect the generalisation capability of the models. Accuracy of the SVR was the highest, however, in nested CV its performance was highly reduced. Therefore, KNN was recommended due to its second highest performance. The results demonstrated that prediction of MDPs from only material properties is possible and promising to use in mixture design. Abbreviations: ANN: artificial neural network; BC: bitumen content; BP: bitumen penetration (1/10 mm); CV: cross-validation; DEM: discrete element method; GA: genetic algorithm; Gmb: Bulk specific gravity of mixture; Gmm: Maximum specific gravity of mixture; Gsb: bulk specific gravity of aggregate; KNN: k nearest neighbour; LA: Los Angeles abrasion; LR: linear regression; MARS: multivariate adaptive regression spline; MDP: Marshall design parameter; MF: Marshall flow; MQ: Marshall quotient (kN/mm); MS: Marshall stability; NMAS: nominal maximum aggregate size; NoB: number of blows; PI: penetration index; PR: polynomial regression; R 2: coefficient of determination; SP: softening point (°C); SVR: support vector regression; UPVT: ultrasonic pulse velocity–time; Va: air voids percentage; VFA: voids filled with asphalt; VMA: voids in mineral aggregate; WA: water absorption.