Applications Review of Hassanat Distance Metric

Hassanat, Ahmad B. A.; Alkafaween, Esra’a; Tarawneh, Ahmad S.; Elmougy, Samir

doi:10.1109/etcea57049.2022.10009844

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…However, further research is needed to critically look at and address the issue of sparsity to enhance the performance of the proposed method. Another limitation of this study is the use of only one distance metric, the Minkowski, which is not always the optimum distance to employ; other distance measures, such as Hassanat [72,73,74], maybe a better option.…”

Section: Discussionmentioning

confidence: 99%

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Tarawneh,

Alamri,

Al-Saedi

et al. 2023

IEEE Access

Self Cite

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Big data classification is a challenging task because most known classification methods need a long time and a lot of processing resources to execute such a task and use the vast amount of available data. In this paper, we propose a novel big data classification method that leverages the power of the KNN classifier and the efficiency of the ensemble learning technique to create a new method capable of performing classification tasks on big data efficiently. The proposed method picks tiny data chunks at random from a big dataset, with each chunk including random examples of a small number of randomly selected features. A weak KNN classifier is employed on each data chunk to perform classification on new (unseen) data, and the majority voting rule is used to reach the final classification decision based on the outcomes of the weak classifiers. The proposed method has a constant classification time, according to the time complexity analysis. Furthermore, the proposed method was found to be more efficient on a single node than existing methods, some of which run on a large cluster of nodes. Because of its speed and enhanced performance, the proposed method can be considered an ideal classifier for handling complex data types such as Geospatial data, Big trajectory data, and Big Data in general. INDEX TERMSBig data; Geospatial data; Trajectory data; Classification; Ensemble learning; KNN I. INTRODUCTIONFor decades, researchers have been studying and evaluating big data methodologies and tools. This interest stems from the massive volume of data exchanged and saved by social media users, medical organizations, educational institutions, and others.According to statistical reports, the number of users on different social media platforms has reached more than 2 billion [1]. WhatsApp, for example, has over 600 million users, more than half a billion photos, and one hundred million videos transferred and shared between users on a daily basis.[2]. Also, due to the huge advance in smartphone technology, it has become easier for users to share text\images and write posts on such social media platforms. Some reports show that the number of posts on Twitter in 2007 was 5K, this number became around 500 million after about 6 years, in 2013 [3], which indicates the massive amount of available data on social media in general. This amount of data is not restricted to social media, as many other platforms generate and store huge data volumes [4,5,6,7].This amount of data needs to be processed and analyzed in order to use it for building useful knowledge discovery and machine learning big data-based applications, like facial big data applications [8,9,10], signal big data [11] and various industry big data-based applications [12,13,14].Volume (big), Variety, and Velocity are among the most distinguishing characteristics of Big data, and as a result, it is attractive to have an efficient classification/prediction system to learn from such Big data. Such applications include, but are not limited to, medical [15,16], financial [17,18,19]...

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Section: Discussionmentioning

confidence: 99%

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Tarawneh,

Alamri,

Al-Saedi

et al. 2023

IEEE Access

Self Cite

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“…This study used four error metrics to evaluate the prediction effect of MSGSGCN. These are MAE, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Hassanat Distance (MHD) [39]. Additionally, this study introduces the MHD as an evaluation metric in the ablation experiments, as it is less sensitive to outliers and better able to accurately assess model performance, where an MHD result closer to 0 indicates a more ideal outcome.…”

Section: Baseline Models and Evaluation Metrics 431 Evaluation Metricsmentioning

confidence: 99%

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

Cao,

Bao,

Shi

et al. 2024

Symmetry

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Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an undirected static adjacency matrix to map the correlation of nodes, which ignores the dynamic symmetry change of correlation over time and faces the challenge of oversmoothing during training iterations, making it difficult to learn the spatial structure and temporal trend of the traffic network. To overcome the above challenges, we propose a novel multi-head self-attention gated spatiotemporal graph convolutional network (MSGSGCN) for traffic speed prediction. The MSGSGCN model mainly consists of the Node Correlation Estimator (NCE) module, the Time Residual Learner (TRL) module, and the Gated Graph Convolutional Fusion (GGCF) module. Specifically, the NCE module aims to capture the dynamic spatiotemporal correlations between nodes. The TRL module utilizes a residual structure to learn the long-term temporal features of traffic data. The GGCF module relies on adaptive diffusion graph convolution and gated recurrent units to learn the key spatial features of traffic data. Experimental analysis on a pair of real-world datasets indicates that the proposed MSGSGCN model enhances prediction accuracy by more than 4% when contrasted with state-of-the-art models.

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“…The result is bounded between 0 and 1, which is highly explainable as a model performance 3. Interpretable: As being based on the well-known Hassanat Distance metric [6][7][8] .…”

Section: Boundedmentioning

confidence: 99%

“…As one can see from Equation 3, HasD is bounded by [0, 1]. It reaches 1 when the maximum value approaches ∞ assuming the minimum is −∞, or when the minimum value approaches −∞ assuming the maximum is ∞ [6][7][8] . HasD between 0 and values in the range of [-10, 10] is visualized in Figure 1 10.0 Also, HasD can be simplified mathematically to the expression in Equation 4.…”

Section: Proposed Metricmentioning

confidence: 99%

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Based on Hassanat Distance Metric

Hassanat,

Alqaralleh,

Tarawneh

et al. 2024

Preprint

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Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression model accuracy measure based on Hassanat distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0-100\%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models' accuracy.

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Applications Review of Hassanat Distance Metric

Cited by 6 publications

References 26 publications

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Based on Hassanat Distance Metric

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