The distance function effect on k-nearest neighbor classification for medical datasets

By Li-Yu Hu, Min-Wei Huang, Shih-Wen Ke, Chih-Fong Tsai. SpringerPlus, 2016

429 citations recorded by the source corpus.

IntroductionK-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.Case descriptionSince the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.Discussion and evaluationThe experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.ConclusionsIn this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Health Information Management

View Paper: https://www.semanticscholar.org/paper/d258b75043376e1809bda00487023c4025e1c7a9

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In Artificial Intelligence

1. N. Al‐Ansari
2. Zeeshan Ahmed
3. Khalid Mohamed
4. Xinqi Dong
5. Ataollah Shirzadi
6. Saman Zeeshan
7. Baharin bin Ahmad
8. Khabat Khosravi

In Computer Vision and Pattern Recognition

1. V. B. Surya Prasath
2. Omar Lasassmeh
3. Haneen Arafat Abu Alfeilat
4. Ahmad Hassanat
5. Alexei Botchkarev

In Health Information Management

1. Zeeshan Ahmed
2. Khalid Mohamed
3. Xinqi Dong
4. Saman Zeeshan

Citations in the corpus

• An Introduction to Machine Learning (2020, background): “…feature vectors of two data points A and B , for features i = 1 … n , but depending on the type of data we are dealing with there can be many other and sometimes much more complex distance or similarity measures, such as cosine similarity 15 or similarity scores of two biological sequences.”
• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Some of real-time examples include modeling diagnostic performance (195), preserving privacy of medical diagnosis in e-Health cloud (196), medical dataset classification (197), classification of lymph node metastasis in gastric cancer (198), pattern classification for breast cancer diagnosis (199), ”
• Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review (2017, background): “Therefore, distance measures play a vital role in determining the final classification output (Hu et al., 2016).”
• Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, methodology): “Remote Sens. 2020, 11, x FOR PEER REVIEW 12 of 30 We used the Classification Learner application in MATLAB R2018a to automatically train a selection of different KNN classification models on a training dataset.”