Robust outlier detection based on the changing rate of directed density ratio

Li, Kangsheng; Gao, Xin; Fu, Shiyuan; Diao, Xinping; Xue, Bing; Yu, Jiahao; Huang, Zijian

doi:10.1016/j.eswa.2022.117988

Cited by 20 publications

(1 citation statement)

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“…Sorting the classes according to a hyperplane that depicts relative relationships among points concerning the influence of space surrounding each point can estimate whether it is a target or an outlier [42]. All these methods are strongly affected by many factors: the number of extracted classes and their belonging clusters [43], the local density estimation and the local reachability among connected points, boundaries that separate clusters [44], and local outliers [45]. These parameters vary with the variety of target datasets [46].…”

Section: Related Workmentioning

confidence: 99%

Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

Hasan

Yusuf

Rahim

et al. 2023

Plants

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The current methods of classifying plan disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%.

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