Mahalanobis distance-based two new feature evaluation criteria

Ray, Swati; Turner, L.F.

doi:10.1016/0020-0255(92)90012-w

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…The classification map is depicted in Figure 4. The process of calculating the Mahalanobis distance requires the overall sample number to be higher than the dimension number of the sample; otherwise, the inverse matrix of the overall sample covariance matrix cannot be obtained [56,57]. Therefore, in this study the classification training samples could not be applied because the overall sample number was lower than the size of the scale, and certified samples were selected for classification and verification.…”

Section: Other Single Classifier Classificationmentioning

confidence: 99%

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Zhao

Zhang

Pan

2017

Mathematical Problems in Engineering

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A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of -means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

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Section: Other Single Classifier Classificationmentioning

confidence: 99%

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Zhao

Zhang

Pan

2017

Mathematical Problems in Engineering

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“…They proposed Mahalanobis distance 1 , 0 2 to reduce the drawback of getting high value of average distance which fails to represent the average separability of g classes. In order to transform Mahalanobis distance , 0 2 to bounded Mahalanobis distance 1 , 0 2 , Ray and Turner (1992) derived the transformed 2 (denoted by 2 1 ) given by equation ( 7), where 1 π and 2 π are the a priori probabilities of the respective classes.…”

Section: Proposed [0 1] Bounded Mahalanobis Distancementioning

confidence: 99%

“…Further details of the transformations, properties as well as some related proofs are referred to Ray and Turner (1992). In sensors closeness test, the transformation 2 1 0, 1 is achieved by applying the concept in equation (7).…”

Section: Proposed [0 1] Bounded Mahalanobis Distancementioning

confidence: 99%

“…In order to overcome these flaws, we adopted a concept of bounded Mahalanobis distance 2 which devoted by Ray and Turner (1992). Their paper mainly discussed the application of Mahalanobis distance-based criteria to evaluate new feature for classification.…”

Section: Introductionmentioning

confidence: 99%

“…Sensors Closeness Test Based on an Improved 0, 1 Bounded Mahalanobis Distance 2 Maz Jamilah Masnan 1, a) , Nor Idayu Mahat 2, b) , Ali Yeon Md Shakaff 3, c) and Abu Hassan Abdullah 3, d)…”

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Sensors closeness test based on an improved [0, 1] bounded Mahalanobis distance Δ2

Masnan

Mahat

Shakaff³

et al. 2015

AIP Conference Proceedings

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Mahalanobis distance values are commonly in the range of 0 to where higher values represent greater distance between class means or points. The increase in Mahalanobis distance is unbounded as the distance multiply. To certain extend, the unbounded distance values pose difficulties in the evaluation and decision for instance in the sensors closeness test. This paper proposes an approach to [0, 1] bounded Mahalanobis distance that enable researcher to easily perform sensors closeness test. The experimental data of four different types of rice based on three different electronic nose sensors namely InSniff, PEN3, and Cyranose320 were analyzed and sensor closeness test seems successfully performed within the [0, 1] bound.

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Improving Classification of Microembolus and Artifact of HITS Event by Feature Selection

Ghazali

Masnan

Ramli

2019

10th International Conference on Robotics, Vision, Signal Processing and Power Applications

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Mahalanobis distance-based two new feature evaluation criteria

Cited by 6 publications

References 29 publications

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Sensors closeness test based on an improved [0, 1] bounded Mahalanobis distance Δ2

Improving Classification of Microembolus and Artifact of HITS Event by Feature Selection

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