On extending F-measure and G-mean metrics to multi-class problems

Espíndola, Rogério Pinto; Ebecken, Nelson F. F.

doi:10.2495/data050031

Cited by 52 publications

(11 citation statements)

References 7 publications

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“…With just 30 features, the classification accuracy of the SVM classifier was more than 94% of the accuracy of the total features. The achieved accuracy outperforms the accuracy of the original study, which used the same dataset [ 60 ], which was 91.7%.…”

Section: Resultsmentioning

confidence: 65%

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Bennasar

Price

Gooch

et al. 2022

Sensors

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Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.

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Section: Resultsmentioning

confidence: 65%

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Bennasar

Price

Gooch

et al. 2022

Sensors

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“…The first experiment proves the superiority of ROA against three other latest optimization algorithms AOA, GWO and WOA by applying data reduction for different 12 datasets obtained from KEEL repository [26]. In the second experiment a recent published paper [27], [28] were used in a comparison with our proposed algorithm. The last experiment was conducted on three student performance prediction data as a real-life application.…”

Section: Methodsmentioning

confidence: 89%

Rhizostoma optimization algorithm and its application in different real-world optimization problems

Mahareek

Çifçi

El-zohni

et al. 2023

IJECE

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<p>In last decade, numerous meta-heuristic algorithms have been proposed for dealing the complexity and difficulty of numerical optimization problems in the realworld which is growing continuously recently, but only a few algorithms have caught researchers’ attention. In this study, a new swarm-based meta-heuristic algorithm called Rhizostoma optimization algorithm (ROA) is proposed for solving the optimization problems based on simulating the social movement of Rhizostoma octopus (barrel jellyfish) in the ocean. ROA is intended to mitigate the two optimization problems of trapping in local optima and slow convergence. ROA is proposed with three different movement strategies (simulated annealing (SA), fast simulated annealing (FSA), and Levy walk (LW)) and tested with 23 standard mathematical benchmark functions, two classical engineering problems, and various real-world datasets including three widely used datasets to predict the students’ performance. Comparing the ROA algorithm with the latest meta-heuristic optimization algorithms and a recent published research proves that ROA is a very competitive algorithm with a high ability in optimization performance with respect to local optima avoidance, the speed of convergence and the exploration/exploitation balance rate, as it is effectively applicable for performing optimization tasks.</p>

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“…In predictive analytics, a confusion matrix is a two-dimensional matrix, with two rows and two columns that report the numbers of true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) [ 46 , 47 , 48 , 49 , 50 ]. In Table 1 , the columns represent the actual classes while the rows represent the predicted classes [ 51 , 52 , 53 ].…”

Section: Results and Evaluationmentioning

confidence: 99%

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

Loukatos¹,

Kondoyanni²,

Alexopoulos³

et al. 2023

Sensors

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The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation.

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On extending F-measure and G-mean metrics to multi-class problems

Cited by 52 publications

References 7 publications

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Rhizostoma optimization algorithm and its application in different real-world optimization problems

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

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