Reduced Distance Computation k Nearest Neighbor Model

Nair, Preeti; Kashyap, Indu

doi:10.26438/ijcse/v7i5.658666

Cited by 2 publications

(2 citation statements)

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“…For example, Li et al set different K values for different categories of data [11]. Nair and Kashyap choose the best K value based on accuracy [12]. Saputra et al determined the optimal K value based on the local structure of the data and accuracy [13].…”

Section: A Classic Knnmentioning

confidence: 99%

Quantum KNN Classification With K Value Selection and Neighbor Selection

Li,

Zhang,

Zhang

et al. 2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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The KNN (K-nearest neighbors) algorithm is one of Top-10 data mining algorithms and is widely used in various fields of artificial intelligence. This leads to that quantum KNN algorithms have developed and achieved certain speed improvements, denoted as Q-KNN. However, these Q-KNN methods must face two key problems as follows. The first one is that they are mainly focused on neighbor selection without paying attention to the influence of K value on the algorithm. The second is that only the neighbor selection process is quantized, and the selection of K value is not quantized. To solve these problems, this paper designs a novel quantum circuit for KNN classification, so as to simultaneously quantumize the neighbor selection and K value selection process. Specifically, the least squares loss and sparse regularization term are first used to construct the objective function of the proposed quantum KNN, so that it can simultaneously obtain the optimal K value and K nearest neighbors of the testing data. And then, a new quantum circuit is proposed to quantumize the process through quantum phase estimation, controlled rotation, and inverse phase estimation techniques. Finally, experiments are conducted with qiskit and matlab to output the quantum and classical results of the algorithm, verifying that the proposed algorithm can output the optimal K value and K nearest neighbors for each testing data.

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Section: A Classic Knnmentioning

confidence: 99%

Quantum KNN Classification With K Value Selection and Neighbor Selection

Li,

Zhang,

Zhang

et al. 2024

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…This approach is common because it is straightforward to comprehend and produces accurate results quickly. The KNN method takes N training vectors as input and determines the k points that are the closest neighbors to the point of interest, regardless of the labels [24]. The KNN algorithm for lung image classification is shown below:…”

Section: K-nearest Neighbormentioning

confidence: 99%

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

Shankara

Hariprasad

Latha

2023

IJECE

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<span lang="EN-US">Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.</span>

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Reduced Distance Computation k Nearest Neighbor Model

Cited by 2 publications

References 0 publications

Quantum KNN Classification With K Value Selection and Neighbor Selection

Quantum KNN Classification With K Value Selection and Neighbor Selection

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

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