Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyperparameters (or decision variables) in a machine learning model (or a black-box function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. Moreover, we have compared the performance of the proposed approach against particle swarm optimization (PSO) and simulated annealing (SA) methods in function optimization to provide additional insights into its exploration in the search space. According to the empirical results, the proposed model outperformed the compared random-based methods in almost all tasks conducted, notably in a higher number of dimensions and in the presence of limited on-device computational resources.

Time Series Classification, is one of the very challenging areas in pattern recognition problems. As the volume of time series data increases, a multitude of TSC algorithms have been developed. Notably, only a limited number of these approaches have ventured into the realm of Deep Neural Networks to tackle this task. This paper presents a new hierarchical deep neural network architecture with a multi-section learning mechanism for the classification of multi-output time series datasets. The hierarchical structure of the proposed approach uses separate segments to detect output classes of different granular levels. The presented model does not suffer from heavy computations during training found in commonly used in other deep learning models such as convolutional neural network. Additionally, our suggested method handles data dimension reduction automatically in the hidden layers of different network sections and does not require separate pre-processing units for this purpose. To demonstrate the effectiveness of the proposed model, we have utilized it in the wearable sensor-based Human Activity Recognition problem and evaluated its performance on several benchmark datasets in TSC and HAR, such as PAMAP2 and Opportunity, with several different configurations. The empirical results on the benchmark datasets show that our proposed model outperforms several other deep learning-based solutions based on measures such as F ₁ -Score, accuracy, and ROC curve.

Artificial Intelligence (AI) continues to evolve rapidly, impacting numerous fields, including time series (TS) classification and human activity recognition (HAR). Despite the advancements in deep learning models, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), these models face several challenges, including the need for extensive labeled datasets, significant computational resources, and lack of interpretability. This research aims to address these limitations by developing an adaptive hierarchical deep neural network framework that integrates fuzzy logic principles and adaptive learning techniques for robust, computationally efficient, and interpretable real-time TS analysis. The reduction in the number of parameters and the efficient learning of hierarchical features mean that less training data is needed to achieve robust performance. The model's ability to generalize from hierarchical representations allows it to make effective use of smaller datasets, which is particularly advantageous in scenarios where data is limited or expensive to obtain.The proposed framework specifically targets HAR applications using data from wearable sensors.

Hyper-parameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general-purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyper-parameters (or decision variables) in a machine learning model (or general function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against the changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. According to the empirical results, the proposed model outperformed the compared methods in the experimented classification, regression, and multi-dimensional function optimization tasks, notably in a higher number of dimensions and in the presence of limited on-device computational resources.