Combining Mahalanobis and Jaccard to Improve Shape Similarity Measurement in Sketch Recognition

Salleh, Siti Salwa; Aziz, Noor Aznimah Abdul; Mohamad, Dzulkifli; Omar, Mohd Nasir

doi:10.1109/uksim.2011.67

Cited by 6 publications

(8 citation statements)

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“…Cases similar to a given case are retrieved according to a similarity score [63,64]. Therefore, measuring the similarity distance among cases is important [41,54,56].…”

Section: Similarity Distance Measurement With Cbrmentioning

confidence: 99%

“…Therefore, measuring the similarity distance among cases is important [41,54,56]. Various distance measurement methods are available, such as the Euclidean distance, Mahalanobis distance, Manhattan distance, arithmetic summation, fractional function, Minkowski distance, Cosine distance, and Jaccard distance [40,46,64]. The Mahalanobis distance refers to a distance between two points in multivariate space, which is widely adopted in the cluster and classification analysis [40,65] because the distance can consider correlated relationships among attributes.…”

Section: Similarity Distance Measurement With Cbrmentioning

confidence: 99%

“…Either of these two similarity measures can be applied to the interval and ratio attributes [40]. Among them, the Euclidean distance is the most common similarity measurement method [40,63,66], and the Jaccard distance is effective for the calculation of the similarity of cases that include attributes, such as binary or text formats [64,67]. Thus, this research considers WED and JED to examine the differences in case similarity depending on the types of attributes and to confirm the effect of distance measurement on the prediction results.…”

Section: Similarity Distance Measurement With Cbrmentioning

confidence: 99%

“…The k-nearest neighbor retrieval, which is a fundamental algorithm for the evaluation of the similarity between the test case and previous cases in CBR, is used to retrieve the k-nearest cases [22,41,55,66]. In addition, the Jaccard distance measurement called the Jaccard similarity coefficient is utilized to determine the similarity of cases, including attributes with a binary or text format [46,64,67]. This method enables the similarity distance among cases to be computed in a simple and fast manner without data redundancy [46,64].…”

Section: Case Retrievalmentioning

confidence: 99%

“…In addition, the Jaccard distance measurement called the Jaccard similarity coefficient is utilized to determine the similarity of cases, including attributes with a binary or text format [46,64,67]. This method enables the similarity distance among cases to be computed in a simple and fast manner without data redundancy [46,64]. The Jaccard coefficient was obtained in the range of 0-1 by determining the shared and different attributes in the datasets.…”

Section: Case Retrievalmentioning

confidence: 99%

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Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

et al. 2019

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Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

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“…Cases similar to a given case are retrieved according to a similarity score [63,64]. Therefore, measuring the similarity distance among cases is important [41,54,56].…”

Section: Similarity Distance Measurement With Cbrmentioning

confidence: 99%