Stacked Generalization with Wrapper-Based Feature Selection for Human Activity Recognition

Bhavan, Anjali; Aggarwal, Swati

doi:10.1109/ssci.2018.8628830

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…These methods are the combination of the best qualities of filter and wrapper methods in which the variable selection process and classification have been implemented simultaneously using a learning algorithm [77]. Assessment of the importance of variables using embedded methods can be referred to in [79]. In this study, six feature selection methods (i.e., two filter methods including Pearson's R (PR) and mutual information (MI), two wrapper methods including Boruta and Stepwise Feature Selection (SFS), and two embedded methods including Random Forest (RF) and Recursive Feature Elimination (RFE)) were used to assess the contribution of variables to ML models.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

2022

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Assessment of the structural condition of sewer pipes is one of the critical steps in asset management and support investment decisions; therefore, structural condition models with high accuracy are important that can help the utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of advanced machine learning (ML) for predicting the structural condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater level, traffic volume, distance from the road, and soil type) were used to assess the sewer structural condition employing seventeen ML models. After processing the sewer inspections, 1,159 of 1,449 individual pipelines were used to train the structural condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the RF model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

2022

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“…This is due to the fact that raw sensor data are always noise-corrupted, which makes it hard to measure and reflect the true motion change of smartphones accurately. After preprocessing the raw data, traditional methods extract a large amount of features and select some principal features [21] representing the essential difference between different activities. Features extracted from the time domain, frequency domain, wavelet energy and interquartile range are extensively used.…”

Section: A Traditional Methods For Harmentioning

confidence: 99%

Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning

et al. 2019

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The ubiquity of smartphones and their rich set of on-board sensors has created many exciting new opportunities, where smartphones are used as powerful computing platforms to sense and analyze pervasive data. One important application of mobile sensing is activity recognition based on smartphone inertial sensors, which is a fundamental building block for a variety of scenarios, such as indoor pedestrian tracking, mobile health care, and smart cities. Although many approaches have been proposed to address the human activity recognition problem, several challenges are still present: 1) people's motion modes are very different for different individuals; 2) there is only a very limited amount of training data; 3) human activities can be arbitrary and complex, thus handcrafted feature engineering often fails to work; and 4) the recognition accuracy tends to be limited due to confusing activities. To tackle those challenges, in this paper, we propose a human activity recognition framework based on convolutional neural networks (CNNs) with two convolutional layers using the smartphone-based accelerometer, gyroscope, and magnetometer. To solve the confusion between highly similar activities like going upstairs and walking, this paper presents a novel ensemble model of CNN to further improve the identification accuracy. The extensive experiments have been conducted using 235 977 sensory samples from a total of 100 subjects. The results have shown that the classification accuracy of the proposed model can be up to 96.11%, which proves the effectiveness of the proposed model. INDEX TERMSConvolutional neural network, human activity recognition, sensor data, smartphone.

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“…The stacked ensemble model can achieve an accuracy of 0.96 for various HAR tasks. HAR task prediction accuracy is further improved in [21] where Boruta, a wrapper-based all-relevant feature selection method is used for feature extraction, before model training. The stacking approach is composed of various machine learning algorithms like the random forest, multi-layer perceptron, logistic regression, and SVM with linear kernel and Boruta shows good performance with an accuracy of 0.97%.…”

Section: Related Workmentioning

confidence: 99%

“…Table 11 contains the results and the models of the above-mentioned studies. [44] Deep CNN 0.946 [45] Deep CNN 0.951 [46] Deep CNN 0.947 [47] Deep CNN 0.900 [48] Deep ConvLSTM 0.958 [16] Residual Bi LSTM 0.905 [17] Multiview stacking 0.925 [18] Stacked LSTM 0.930 [19] SDAE+GBM 0.959 [20] Stacked ensemble 0.960 [21] Stacked ensemble 0.968 DS-MLP Deep Stacked Ensemble 0.973 Performance analysis suggests that the approaches based on deep CNN have an accuracy between 0.90 to 0.95. Ensemble approaches, on the other hand, tend to have higher accuracy than that of simple deep learning approaches.…”

Section: Performance Comparison With State-of-the-art Studiesmentioning

confidence: 99%

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

et al. 2020

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The recent development of machines exhibiting intelligent characteristics involves numerous techniques including computer hardware and software architecture development. Many different hardware devices, wearable sensors, machine learning, and deep learning model implementations are being applied in human activity recognition (HAR) applications in recent times. However, to develop high accuracy classification systems for activity recognition using low-cost hardware technology is of significant importance. To achieve this goal this study uses sensor data from two low-cost sensors, gyroscope and accelerometer along with the implementation of an Artificial Neural Network (ANN) based deep learning model for HAR. In particular, Deep Stacked Multilayered Perceptron (DS-MLP) has been proposed. In the implementation of DS-MLP, an ANN model has been used as a meta-learner while five MLP models have been used as base-learners. In this study, these base-learners and meta-learner have been combined using a stack ensemble technique. The performance evaluations have been done first on the applicability of individual base-models followed by the application of DS-MLP, the results prove the high accuracy of 97.3% and 99.4% for heterogeneous datasets used for testing. The performance of the proposed DS-MLP models has been compared to some existing machine learning classifiers and several state-of-the-art activity recognition systems. The comparative result analysis also proves that the proposed system performed better than these classification approaches in terms of important performance metrics such as accuracy, precision, recall, F-score, Cohen's Kappa, and Mathew correlation coefficient.

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Stacked Generalization with Wrapper-Based Feature Selection for Human Activity Recognition

Cited by 9 publications

References 14 publications

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

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