Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

[1]  Rossitza Goleva,et al.  Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering , 2017, IEEE Access.

[2]  Mengjie Zhang,et al.  Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms , 2014, Appl. Soft Comput..

[3]  Luca Palmerini,et al.  Physical Activity Classification for Elderly People in Free-Living Conditions , 2019, IEEE Journal of Biomedical and Health Informatics.

[4]  Wei Jiang,et al.  k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[5]  Richard Weber,et al.  Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines , 2014, Inf. Sci..

[6]  Wlodek Kulesza,et al.  IoT-based information system for healthcare application : Design methodology approach , 2017 .

[7]  Wala Saadeh,et al.  A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Abdul Rahim Abdullah,et al.  A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection , 2019, Informatics.

[9]  Xiaodong Li,et al.  An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[10]  Jun Zhang,et al.  Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems , 2018, IEEE Transactions on Evolutionary Computation.

[11]  Zoubin Ghahramani,et al.  Human Activity Recognition by Combining a Small Number of Classifiers , 2016, IEEE Journal of Biomedical and Health Informatics.

[12]  Mohammad Hossein Moattar,et al.  Evolutionary feature subsets selection based on interaction information for high dimensional imbalanced data classification , 2019, Appl. Soft Comput..

[13]  Sungyoung Lee,et al.  Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition , 2018, Sensors.

[14]  Yitian Xu,et al.  Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification , 2017, IEEE Transactions on Cybernetics.

[15]  Ke Chen,et al.  Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection , 2019, Expert Syst. Appl..

[16]  Xuewen Xia,et al.  Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory , 2019, IEEE Access.

[17]  Lars Bauer,et al.  From Cloud Down to Things: An Overview of Machine Learning in Internet of Things , 2019, IEEE Internet of Things Journal.

[18]  Macarena Espinilla,et al.  Sensor-Based Datasets for Human Activity Recognition – A Systematic Review of Literature , 2018, IEEE Access.

[19]  Yong Luo,et al.  Cost-Sensitive Feature Selection by Optimizing F-Measures , 2018, IEEE Transactions on Image Processing.

[20]  Leonardo Feltrin.  KNIME an Open Source Solution for Predictive Analytics in the Geosciences [Software and Data Sets] , 2015, IEEE Geoscience and Remote Sensing Magazine.

[21]  Dominik Schuldhaus,et al.  Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset , 2013, PloS one.

[22]  David Dagan Feng,et al.  An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification , 2017, IEEE Journal of Biomedical and Health Informatics.

[23]  Julio López,et al.  Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification , 2018, Appl. Soft Comput..

[24]  Xin Fan,et al.  Feature selection for imbalanced data based on neighborhood rough sets , 2019, Inf. Sci..

[25]  Eleman Teitei,et al.  Biased Random Forest For Dealing With the Class Imbalance Problem , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Claudio Savaglio,et al.  A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare , 2020, Inf. Fusion.

[27]  Enver Ever,et al.  Activity Recognition Using Binary Sensors for Elderly People Living Alone: Scanpath Trend Analysis Approach , 2019, IEEE Sensors Journal.

[28]  Hiroshi Mitoma,et al.  Relationship between Amount of Daily Movement Measured by a Triaxial Accelerometer and Motor Symptoms in Patients with Parkinson’s Disease , 2017 .

[29]  Ahmad Lotfi,et al.  A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living , 2019, Appl. Soft Comput..

[30]  Xindong Wu,et al.  Online feature selection for high-dimensional class-imbalanced data , 2017, Knowl. Based Syst..

[31]  António Pacheco,et al.  Theoretical foundations of forward feature selection methods based on mutual information , 2017, Neurocomputing.

[32]  Matthias Pätzold,et al.  A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition , 2019, IEEE Access.

[33]  Min Du,et al.  Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization , 2019, IEEE Access.

[34]  Rytis Maskeliunas,et al.  A Review of Internet of Things Technologies for Ambient Assisted Living Environments , 2019, Future Internet.

[35]  Cornelia Schneider,et al.  Measuring the Use of the Active and Assisted Living Prototype CARIMO for Home Care Service Users: Evaluation Framework and Results , 2019 .

[36]  Pendar Alirezazadeh,et al.  A Genetic Algorithm-Based Feature Selection for Kinship Verification , 2015, IEEE Signal Processing Letters.

[37]  Parham Moradi,et al.  A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy , 2016, Appl. Soft Comput..

[38]  Dunwei Gong,et al.  Binary differential evolution with self-learning for multi-objective feature selection , 2020, Inf. Sci..