A new concept of higher-order similarity and the role of distance/similarity measures in local classification methods

Todeschini, Roberto; Ballabio, Davide; Consonni, Viviana; Grisoni, Francesca

doi:10.1016/j.chemolab.2016.06.013

Cited by 30 publications

(8 citation statements)

References 34 publications

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“…Todeschini et al 22,23 analyzed the effect of 18 different distance measures on the performance of KNN classifier using eight benchmark data sets. The investigated distance measures included MD, ED, Soergel distance (SoD), Lance-Williams distance, contracted Jaccard-Tanimoto distance, Jaccard-Tanimoto distance, Bhattacharyya distance (BD), Lagrange distance, Mahalanobis distance, Canberra distance (CanD), Wave-Edge distance, Clark distance (ClaD), CosD, CorD, and four locally centered Mahalanobis distances.…”

Section: Introductionmentioning

confidence: 99%

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

et al. 2019

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The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision, and recall) of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only *20% while the noise level reaches 90%, this is true for most of the distances used as well. This means that the KNN classifier using any of the top 10 distances tolerates noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing with other distances.

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Section: Introductionmentioning

confidence: 99%

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

et al. 2019

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“…For example, in [28] it was showed that the Manhattan distance outperforms Chebyshev and Euclidean metrics on intrusion data. Good performance of the Manhattan distance was also demonstrated in [29,30].…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 78%

Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms

et al. 2021

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One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

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“…1 According to [21], the k-NN classifier can be used to classify new data objects using only their distance to labelled samples. However, some works consider any metric or non-metric measures used with this classifier: several studies have been conducted to evaluate the k-NN classifier using different metric and non-metric measures such as the studies presented in [7,10,[22][23][24][25][26].…”

Section: K-nearest Neighbour Classifier (K-nn)mentioning

confidence: 99%

“…However, k-NN can also be applied to other type of data includes categorical data [6]. Several investigations have been done to find a proper categorical measures for such data, such as the works presented in [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets

2019

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Distance-based algorithms are widely used for data classification problems. The k-nearest neighbour classification (k-NN) is one of the most popular distance-based algorithms. This classification is based on measuring the distances between the test sample and the training samples to determine the final classification output. The traditional k-NN classifier works naturally with numerical data. The main objective of this paper is to investigate the performance of k-NN on heterogeneous datasets, where data can be described as a mixture of numerical and categorical features. For the sake of simplicity, this work considers only one type of categorical data, which is binary data. In this paper, several similarity measures have been defined based on a combination between well-known distances for both numerical and binary data, and to investigate k-NN performances for classifying such heterogeneous data sets. The experiments used six heterogeneous datasets from different domains and two categories of measures. Experimental results showed that the proposed measures performed better for heterogeneous data than Euclidean distance, and that the challenges raised by the nature of heterogeneous data need personalised similarity measures adapted to the data characteristics.

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A new concept of higher-order similarity and the role of distance/similarity measures in local classification methods

Cited by 30 publications

References 34 publications

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms

Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets

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