Weighted Chebyshev distance classification method for hyperspectral imaging

Demirci, S.; Erer, İşın; Ersoy, Okan K.

doi:10.1117/12.2181914

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…For example, a combination of Jaccard and weighted Euclidean distances was presented for noise prediction [27]. A weighted Chebyshev distance method was proposed for the classification of hyperspectral imagery [28]. In personalized learning application domains, objective distance (OD) [29] was initially proposed to measure the distance between the current competency of a student and the expected level to attain learning expectations.…”

Section: B Prediction Methodsmentioning

confidence: 99%

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2021

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Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

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Section: B Prediction Methodsmentioning

confidence: 99%

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

2021

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“…According to the optimization results, the optimum performance rates for AVIRIS, BOTS, and KSC data are obtained with p-values of 0.60, 0.55, and 0.59, respectively. As a result, the value of p can be chosen as 0.6, which is nearly the best value for the three data sets examined (Demirci et al, 2015). The objective distance (OD) was first suggested in the customized learning application areas to assess the distance between a student's current competency and the required level to meet learning goals.…”

Section: Literature Reviewmentioning

confidence: 99%

Type-II Diabetes Prediction by using Classification and Novel based Method (AWOD)

Farooq¹,

Abid²,

Akbar³

et al. 2022

JCBI

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Type II diabetes is the deadliest disease. It must be identified early to be cured. Prediction models for detection systems typically use common parameters that might not be suitable for all individuals with various health conditions. As a result, this study suggests a way for diabetes type II prediction using variables that reflect individual health issues. More specifically, this work proposes a unique prediction method called Average Weighted Objective Distance (AWOD) based on the idea that the person has a variety of health states coming from a variety of individual characteristics, which is a need for an efficient prediction model. Using information gain to expose significant and inconsequential individual components with varying priorities, denoted by distinct weights (AWOD), modifies Weighted Objective Distance (WOD). To calculate AWOD, the data set is split into a training set and a testing set. The training set is utilized to establish all significant thresholds and constant values. In particular, binary classification issues with a small dataset are developed by AWOD. Two open-source datasets, Pima Indians Diabetes (First Dataset) and Mendeley Data for Diabetes (Second dataset) are examined to validate the suggested methodology. With machine learning-based prediction techniques like (K-Nearest Neighbors, Support Vector Machines, and Random Forest), the prediction performance for both datasets is compared, including statistical measures: Accuracy, Sensitivity, and specificity. The AUC-ROC curve graph reveals how well the model can differentiate across classes for all ML classifiers. The comparison findings demonstrated that the ML classifiers resulted in poor accuracy for the first Dataset. Although better for the second Dataset, the proposed method had greater accuracy than other machine learning based methods, with 95.26 percent and 99.01 percent for Datasets I and II, respectively.

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“…For the Manhattan distance and the Chebyshev distance, they both have the same drawbacks as the Euclidean distance [51,52,53,54,55,56]. When the distances between vectors are the same, these measures can hardly tell which vector is more similar to the target.…”

Section: Related Workmentioning

confidence: 99%

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Zhai

Ortega

Castillejo

et al. 2019

Sensors

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Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.

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Weighted Chebyshev distance classification method for hyperspectral imaging

Cited by 8 publications

References 12 publications

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Type-II Diabetes Prediction by using Classification and Novel based Method (AWOD)

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

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