A review on classification of imbalanced data for wireless sensor networks

Patel, Harshita; Rajput, Dharmendra Singh; Reddy, G. Thippa; Iwendi, Celestine; Bashir, Ali Kashif; Jo, Ohyun

doi:10.1177/1550147720916404

Cited by 108 publications

(67 citation statements)

References 92 publications

(89 reference statements)

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“…In the literature, three categories of techniques have been applied to address the class imbalance problem. These approaches are classified into three types, namely data-based approaches, algorithm-based approaches, and the hybrid approach that combines both [ 33 ]. We adopted this approach to enhance the recognition accuracy of the minority classes.…”

Section: Methodsmentioning

confidence: 99%

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Otebolaku

Enamamu

Alfoudi

et al. 2020

Sensors

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With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy.

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Section: Methodsmentioning

confidence: 99%

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Otebolaku

Enamamu

Alfoudi

et al. 2020

Sensors

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“…In the resent past, machine learning models are very popular to solve various problems like image classification [11], text processing [12], real-time fault diagnosis [13] and healthcare [14,15]. It is very common to use ML algorithms to address disease prediction [16,17] [18].…”

Section: Related Workmentioning

confidence: 99%

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

et al. 2020

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Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. Major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. In order to extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extract different features, fusing them using 1-D pooling, and cross pooling lead to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that, DNN with dropout at input layer converges faster when trained using blended features, than compared to the same model trained using uni-modal deep features.

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“…An optimized binary classification was developed using a modified MTS(MMTS) method. The MMTS showed better results compared with the results obtained from Support Vector Machine(SVM), Probabilistic MTS, Naive Bayes, Hidden Naive Bayes, Kernel Boundary Alignment, Adaptive Conformal Transformation and Synthetic Minority Oversampling Technique methods [24]- [26]. A novel method was developed for the identification of conditions of roads using MTS, where it was applied to classify the quality of roads in cities [27].…”

Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

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Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signalto-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twentysix input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

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A review on classification of imbalanced data for wireless sensor networks

Cited by 108 publications

References 92 publications

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

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