Adaptive Selection Method Research Articles

IRT Models for Learning With Item-Specific Learning Parameters

We propose a new item response theory growth model with item-specific learning parameters, or ISLP, and two variations of this model. In the ISLP model, either items or blocks of items have their own learning parameters. This model may be used to improve the efficiency of learning in a formative assessment. We show ways that the ISLP model’s learning parameters can be estimated in simulation using Markov chain Monte Carlo (MCMC), demonstrate a way that the model could be used in the context of adaptive item selection to increase the rate of learning, and estimate the learning parameters in an empirical data analysis using the ISLP. In the simulation studies, the one-parameter logistic model was used as the measurement model to generate random response data with various test lengths and sample sizes. Ability growth was modeled with a few variations of the ISLP model, and it was verified that the parameters were accurately recovered. Secondly, we generated data using the linear logistic test model with known Q-matrix structure for the item difficulties. Using a two-step procedure gave very comparable results for the estimation of the learning parameters even when item difficulties were unknown. The potential benefit of using an adaptive selection method in conjunction with the ISLP model was shown by comparing total improvement in the examinees’ ability parameter to two other methods of item selection that do not utilize this growth model. If the ISLP holds, adaptive item selection consistently led to larger improvements over the other methods. A real data application of the ISLP was given to illustrate its use in a spatial reasoning study designed to promote learning. In this study, interventions were given after each block of ten items to increase ability. Learning parameters were estimated using MCMC.

Adaptive Feature Selection With Augmented Attributes.

In many dynamic environment applications, with the evolution of data collection ways, the data attributes are incremental and the samples are stored with accumulated feature spaces gradually. For instance, in the neuroimaging-based diagnosis of neuropsychiatric disorders, with emerging of diverse testing ways, we get more brain image features over time. The accumulation of different types of features will unavoidably bring difficulties in manipulating the high-dimensional data. It is challenging to design an algorithm to select valuable features in this feature incremental scenario. To address this important but rarely studied problem, we propose a novel Adaptive Feature Selection method (AFS). It enables the reusability of the feature selection model trained on previous features and adapts it to fit the feature selection requirements on all features automatically. Besides, an ideal l0-norm sparse constraint for feature selection is imposed with a proposed effective solving strategy. We present the theoretical analyses about the generalization bound and convergence behavior. After tackling this problem in a one-shot case, we extend it to the multi-shot scenario. Plenty of experimental results demonstrate the effectiveness of reusing previous features and the superior of l0-norm constraint in various aspects, together with its effectiveness in discriminating schizophrenic patients from healthy controls.

An adaptive selective ensemble algorithm for fault classification

With the aim of improving the classification performance of a single extreme learning machine (ELM) for fault diagnosis, an adaptive selective ensemble classification algorithm is proposed based on the idea of ensemble learning. The proposed algorithm achieves better classification performance than a single model and the selected ELM subnetworks are selected adaptively and the ensemble weights of selected ELM subnetworks are calculated adaptively, which avoids the complex process of manually selecting subnetworks and calculating ensemble weights. First, a regularized error weight adjustment ELM is constructed by introducing regularization parameters and error weight adjustment matrix to a standard ELM, where the error weight adjustment matrix is obtained by the method of adaptive Gaussian kernel density estimation. Then, discrepancy subnetworks are constructed using six different activation functionsand the ensemble weights of subnetworks are obtained adaptively according to the normalized ratio of mean and variance of subnetwork F-scores to achieve the ensemble of subnetworks. Finally, the adaptive selective ensemble classification algorithm is validated using the UCI dataset and experimental acoustic emission signals of gearbox faults. The results show that the adaptive selective ensemble method can improve the stability and accuracy of classification algorithms and the achieved classification accuracy for experimental acoustic emission was 0.9773.

A Maximally Split and Adaptive Relaxed Alternating Direction Method of Multipliers for Regularized Extreme Learning Machines

One of the significant features of extreme learning machines (ELMs) is their fast convergence. However, in the big data environment, the ELM based on the Moore–Penrose matrix inverse still suffers from excessive calculation loads. Leveraging the decomposability of the alternating direction method of multipliers (ADMM), a convex model-fitting problem can be split into a set of sub-problems which can be executed in parallel. Using a maximally splitting technique and a relaxation technique, the sub-problems can be split into multiple univariate sub-problems. On this basis, we propose an adaptive parameter selection method that automatically tunes the key algorithm parameters during training. To confirm the effectiveness of this algorithm, experiments are conducted on eight classification datasets. We have verified the effectiveness of this algorithm in terms of the number of iterations, computation time, and acceleration ratios. The results show that the method proposed by this paper can greatly improve the speed of data processing while increasing the parallelism.

Edge-wise funnel output synchronization of heterogeneous agents with relative degree one

When a group of heterogeneous node dynamics are diffusively coupled with a high coupling gain, the group exhibits a collective emergent behavior which is governed by a simple algebraic average of the node dynamics called the blended dynamics. This finding has been utilized for designing heterogeneous multi-agent systems by building the desired blended dynamics first and then splitting it into the node dynamics. However, to compute the magnitude of the coupling gain, each agent needs to know global information such as the number of participating nodes, the graph structure, and so on, which prevents a fully decentralized design of the node dynamics in conjunction with the coupling laws. To resolve this issue, the idea of funnel control, which is a method for adaptive gain selection, can be exploited for a node-wise coupling, but the price to pay is that the collective emergent behavior is no longer governed by a simple average of the node dynamics. Our analysis reveals that this drawback can be avoided by an edge-wise design premise, which is the idea that we present in this paper. After all, we gain benefits such as a fully decentralized design without global information, collective emergent behavior being governed by the blended dynamics, and the plug-and-play operation based on edge-wise handshaking between two nodes.

An adaptive stochastic ranking-based tournament selection method for differential evolution

An adaptive stochastic ranking-based tournament selection method for differential evolution

ROBUST ONLINE MULTIBAND DRIFT ESTIMATION IN ELECTROPHYSIOLOGY DATA.

High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.

An Adaptive Selection Method for Shape Parameters in MQ-RBF Interpolation for Two-Dimensional Scattered Data and Its Application to Integral Equation Solving

The paper proposes an adaptive selection method for the shape parameter in the multi-quadratic radial basis function (MQ-RBF) interpolation of two-dimensional (2D) scattered data and achieves good performance in solving integral equations (O-MQRBF). The effectiveness of MQ-RBF interpolation for 2D scattered data largely depends on the choice of the shape parameter. However, currently, the most appropriate parameter is chosen by empirical techniques or trial and error, and there is no widely accepted method. Fourier transform can linearly represent 2D scattering data as a combination of sine and cosine functions. Therefore, the paper employs an improved stochastic walk optimization algorithm to determine the optimal shape parameters for sine functions and their linear combinations, generating a dataset. Based on this dataset, the paper trains a particle swarm optimization backpropagation neural network (PSO-BP) to construct an optimal shape parameter selection model. The adaptive model accurately predicts the ideal shape parameters of the Fourier expansion of 2D scattering data, significantly reducing computational cost and improving interpolation accuracy. The adaptive method forms the basis of the O-MQRBF algorithm for solving one-dimensional integral equations. Compared with traditional methods, this algorithm significantly improves the precision of the solution. Overall, this study greatly facilitates the development of MQ-RBF interpolation technology and its widespread use in solving integral equations.

Robust communication-efficient decentralized learning with heterogeneity

In this paper, we propose a robust communication-efficient decentralized learning algorithm, named RCEDL, to address data heterogeneity, communication heterogeneity and communication efficiency simultaneously in real-world scenarios. To the best of our knowledge, this is the first work to address the above challenges in a united framework. In detail, we design a compressed cross-gradient aggregation mechanism with delay to resolve the Non-IID issues, a blocking-resilient mechanism which allows receiving delayed parameters and gradients, and a communication-efficient mechanism including parameters compression and adaptive neighbors selection methods to reduce the communication cost as much as possible. In addition, we also provide convergence analysis of RCEDL and prove its convergence rate O(1NK) same with the state-of-the-art decentralized learning algorithms. Finally, we conduct extensive experiments to evaluate RCEDL algorithm on two widely used datasets CIFAR-10 and MNIST under different experimental settings. Compared with the state-of-the-art baseline methods, the proposed RCEDL is much more robust with higher accuracy and at least 3.4 × communication cost reduction under the heterogeneous environment.

An adaptive key selection method for the multilevel index model for effective service management in the cloud

SUMMARYThe growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning.

The effect of individual-level adaptive stimulus selection on the group-level parameters for cognitive models

In psychological experiments, researchers’ primary interests often gravitate toward group-level parameters, such as the mean difference between experimental conditions. However, existing studies on adaptive stimulus selection have essentially focused on individual-level parameters. Hence, the present study investigated the effect of adaptive stimulus selection on group-level parameters. Two simulation studies indicated that the individual-level adaptive stimulus selection led to higher estimation precision of group-level parameters than random stimulus selection on elaborated decision-making models. Based on these results, it is suggested that individual-level adaptive stimulus selection methods be adopted even cases where the research interest leans toward group-level parameters.

Cross‐project defect prediction method based on genetic algorithm feature selection

AbstractWith the continuous development of Internet technology, the role of software in life is increasing, and software defect prediction (SDP) is a key means to ensure software reliability. SDP is to predict the modules that may have defects in advance based on the historical data of software projects, and its purpose is to maximize the use of testing resources. However, in the actual development process, the project that needs to be predicted is often a new project for which there is little or no historical data. Therefore, how to use the massive data of other related projects to build a cross‐project software defect prediction (CPDP) model has received extensive attention from scholars. However, due to the differences in data distribution and class imbalance between different projects, the performance of CPDP is greatly affected. Therefore, on the basis of CPDP, this article proposes a feature selection method based on genetic algorithm (genetic algorithm feature selection, GAFS). GAFS mainly includes two stages: feature selection and ensemble training. In the feature selection stage, this article proposes a global search adaptive feature selection method based on genetic algorithm, which uses the integrated training results of candidate feature subsets on target data to migrate the optimal feature subset. In the ensemble training phase, the EasyEnsemble method is used to alleviate the class imbalance problem, multiple naive Bayesian classifiers are constructed, and then the final model is constructed through ensemble learning. In this article, F1‐score and MCC are used as the test indicator, and comparative experiments are carried out on AEEEM and Promise. The results show that compared with the five comparison methods, GAFS can improve the average F1‐score and MCC much more. For example, GAFS can improve the average F1‐score value by 38.9%, 31.6%, 35.1%, 22.0%, and 31.6%, respectively. In most cases, it can effectively improve the performance of the model and achieve better prediction results.

Research on Defect Detection of The Liquid Bag of Bag Infusion Sets Based on Machine Vision

Intravenous infusion often uses bag infusion devices for clinic treatment, and the liquid bag assembly is an essential component of the bag infusion device. This paper adopts a machine vision system to inspect the assembly quality of the pipeline and dosing interface of liquid bag assembly. We conduct in-depth research on the lighting method, image pre-processing, and defect detection algorithm of a vision system for two defects of pipeline missing and dosing interface missing. Moreover, we do a lighting experiment to detect transparent liquid bag assembly defects. Proposes an adaptive gamma correction library based on power function derivative calculation, and combines the cumulative histogram for adaptive gamma correction, which amplifies the image edge information, combined with the binarization of the OTSU, solves the image segmentation problem in this. Proposes an adaptive ROI region selection method and virtual linear scanning method to achieve the detection of two kinds of defects. The results show that the recognition rate of missing defects of the pipeline on the liquid bag assembly in the machine vision system reaches 100%.

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction.

To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.

Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach.

Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.

An adaptive divided-difference perturbation method for solving stochastic problems

Most stochastic problems are based on the independent and symmetrically distributed random variables. Using this principle, in this study, we propose an efficient calculation scheme to obtain the statistical moments based on high-order stochastic perturbation (SP) expansion. This solves the long-existing problem of the SP method, namely, the low calculation efficiency of statistical moments using high-order perturbation expansion, and the complexity of the calculation format. The scheme involves adopting the divided-difference method to approximate the partial derivative items of the SP method. The proposed method, which is similar to the collocation point method, only requires the deterministic calculation result for each node in the divided-difference method. In addition, as the adoption of high-order perturbation expansion is limited in multidimensional random problems due to the curse of dimensionality, an adaptive partial derivative items selection method is proposed in this paper to efficiently and reasonably ignore the expansion items that have an insignificant influence on statistical moments. This considerably improves the calculation efficiency of the statistical moments. Finally, the adaptive divided-difference perturbation (ADDP) method proposed in this paper is compared with the quasi-Monte Carlo method and the adaptive sparse grid methods using three numerical examples. The results prove that the ADDP method not only has high efficiency but also leads to significant improvements in the accuracy of statistical moments.

Optimum adaptive bandwidth selection method of local fitting in kernel regression analysis for non-uniform data

Optimum adaptive bandwidth selection method of local fitting in kernel regression analysis for non-uniform data

Multi-Head Attention Network with Adaptive Feature Selection for RUL Predictions of Gradually Degrading Equipment

A multi-head-attention-network-based method is proposed for effective information extraction from multidimensional data to accurately predict the remaining useful life (RUL) of gradually degrading equipment. The multidimensional features of the desired equipment were evaluated using a comprehensive evaluation index, constructed of discrete coefficients, based on correlation, monotonicity, and robustness. For information extraction, the optimal feature subset, determined by the adaptive feature selection method, was input into the multi-head temporal convolution network–bidirectional long short-term memory (TCN-BILSTM) network. Each feature was individually mined to avoid the loss of information. The effectiveness of our proposed RUL prediction method was verified using the NASA IMS bearings dataset and C-MAPSS aeroengines dataset. The results indicate the superiority of our method for the RUL prediction of gradually degrading equipment compared to other mainstream machine learning methods.

Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved LightGBM

The turnout switch machine is the critical equipment of the signal system, which has a significant influence on the efficiency and safety of train operation. However, most fault diagnosis technologies of the switch machine are difficult to distinguish samples with similar categories, which leads to the low diagnostic accuracy. Thus, a fault diagnosis method based on improved LightGBM is proposed to deal with the above problems. Time domain features and multi-scale permutation entropy are extracted to capture the weak fault. Moreover, an adaptive feature selection (AFS) method is presented to reduce redundant features. Especially an improved Focal Loss (IFL) function is established, which improves the ability to distinguish samples of similar features in a multi-classification model. The three-phase action current from the switch machine is utilized to testify to the proposed method and compare it with other methods. The experimental results show that the diagnosis accuracies of this method in the normal-reverse and reverse-normal conversion process reach 98.47 % and 96.09 %, respectively, which is well-suitable for practical application.

Multiple object tracking with behavior detection in crowded scenes using deep learning

Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object-tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%.