Automatic Selection Algorithm Research Articles

Information System for the Selection of Hydropneumatic Capacitive Vessels

This article describes an algorithm for the automatic selection of hydropneumatic capacitive vessels used for household and industrial purposes. The suggested informational system makes it possible to reduce the working time spent on the selection of equipment and increase the relevance of the analyzed information. The article discusses a detailed description of the design of pneumohydroaccumulators, as well as provides examples of calculations of their main characteristics. The main task we needed to solve was a multi-criteria choice, and the article provides an overview of methods for solving this problem. In addition to these methods, we analyzed information systems that allow one to select suitable equipment among various alternatives. Based on the analysis of methods and similar software solutions, the authors suggested an information system and an algorithm for automatic selection of hydraulic accumulators by searching for options that meet the parameters specified by the system user. Also, the article discusses the process of purchasing capacitive equipment. There was demonstrated a logical model of the database structure storing the technical characteristics of the equipment which reflects objects of the information system and their connections. In this article we presented and argued the software for development of a specified information system. The development of the information system was based on the "1С platform: The enterprise". The task of selecting hydropneumatic capacitive vessels was solved by a request that can be interacted with through a report in the information system. This report allows the user to search for equipment by quantitative and qualitative characteristics: volume, operating pressure, vessel material, flange material, installation type (vertical or horizontal), availability of legs, cost, manufacturer or supplier. The report options allow users to search for equipment both in accordance with the specified parameters and with a deviation from them but only if the parameter is quantitative. The results of the report are ordered by relevance to the criteria specified by the user. The operation of the report query aggregating data was reflected in the flowchart. The article presents a situation where the user solves the selection problem by using capabilities of the report. The problem of multi-criteria selection was formulated in a way to demonstrate how a clear parameter of the report becomes fuzzy, and our solution of it allows you to find more alternative options with sorting by relevance helping you to identify the most suitable pneumohydroaccumulator. This software package was registered in the state registry of computer programs.

Automatic constraint programming solver selection method based on machine learning for the cable tree wiring problem

AbstractCable trees are primarily employed in industrial products to facilitate energy transfer and information exchange among various components. When utilizing machines for assembly, it is essential to convert the wiring plan into a sequence of cable insertion operations executed by the machine under various constraints. This poses a combinatorial optimization problem. In this domain, constraint programming (CP) solvers often exhibit outstanding performance by leveraging their robust problem‐modelling capabilities, excellent scalability, and precise solving capabilities. However, CP solvers may achieve various performances for different problem instances. Selecting the most suitable CP solver for each problem instance is crucial. This paper introduces an automatic selection algorithm for CP solvers to solve the cable tree wiring problem (CTW). Firstly, a scoring system is used to conduct an in‐depth analysis and compare four well‐known CP solvers: CPLEX, Chuffed, OR‐Tools, and Gurobi. The results indicate that OR‐Tools and CPLEX outperform other solvers in performance. Moreover, these two solvers exhibit complementary advantages in quickly finding optimal and feasible solutions within specified time limits. Therefore, CP and machine learning are ingeniously integrated, harnessing their complementary advantages. 4240 instances covering various scenarios are randomly generated to form the problem space. This method incorporates decision trees, random forests, K‐nearest neighbours, and naive Bayes, utilizing these four machine learning techniques. The proposed method can achieve better results than traditional single CP solvers. Among all the evaluated machining learning techniques, the automatic solver selection methods based on decision trees and random forests can achieve accuracy rates of 91.29% and 84.15%, respectively.

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.

The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.

Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients

IntroductionThe importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.MethodsA total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebrae and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. Results were validated on an external independent group of CT scans.ResultsThe algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets.ConclusionsOur deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.

AAUConvNeXt: Enhancing Crop Lodging Segmentation with Optimized Deep Learning Architectures

Rice lodging, a phenomenon precipitated by environmental factors or crop characteristics, presents a substantial challenge in agricultural production, notably impacting yield prediction and disaster assessment. Despite that the application of conventional methodologies like visual assessment, mathematical models, and satellite remote sensing technologies has been employed in the segmentation of crop lodging, these approaches are still constrained in precision, immediacy, and capacity for large-scale evaluation. This study introduces an innovative convolutional neural network architecture, AFOA + APOM + UConvNeXt, that integrates intelligent optimization algorithms for automatic selection of optimal network parameters, thereby enhancing the accuracy and efficiency of crop lodging segmentation. The proposed model, empirically validated, outperforms recent state-of-the-art models in crop lodging segmentation, demonstrating higher accuracy, lower computational resource requirements, and greater efficiency, thereby markedly reducing the cost of segmentation. In addition, we investigated the segmentation on half lodging rice, and the results indicate that the model exhibits commendable performance on the half lodging dataset. This outcome holds significant implications for the prediction of rice lodging trends. The fusion of deep learning with intelligent optimization algorithms in this study offers a new effective tool for crop lodging monitoring in agricultural production, providing strong technical support for accurate crop phenotypic information extraction, and is expected to play a significant role in agricultural production practices.

Anytime Automatic Algorithm Selection for the Multi-Agent Path Finding Problem

Anytime Automatic Algorithm Selection for the Multi-Agent Path Finding Problem

Automated algorithm selection using meta-learning and pre-trained deep convolution neural networks

Automated machine learning (AutoML), which explores the automation of various machine learning tasks, has seen significant progress in recent years. One of the fundamental tasks in this field is the selection of effective algorithms for a given dataset. This task is particularly challenging for tabular datasets where, unlike images or text, one cannot use pre-trained embeddings from other datasets. In this study, we present AutoIRAD, a novel meta-learning and vision-based approach for automatic classification algorithm selection for tabular datasets. Our approach is the first to generate image-based representations of entire tabular datasets, enabling us to model the features and interactions of all the dataset samples. Our image-based representation has two significant advantages. First, training our model across multiple datasets enables it to simultaneously learn from diverse domains and create more generalized and effective dataset representations. This representation can also be effectively applied to previously unseen datasets. Secondly, using images enables us to leverage large pre-trained networks (e.g., VGG). By tuning existing networks rather than training new ones, we can both speed up the training of our model and reduce the needed amounts of training data. An extensive evaluation of 150 datasets shows that AutoIRAD achieves results comparable to the leading state-of-the-art AutoML solution but at a fraction of their computation time.

A Computational Tool for Recoloring Based on User Emotions

This work describes a system to recolor a user’s painting based on the perceived emotional state of the viewer. An automatic palette selection algorithm is used to generate color palettes for a set of emotions. A user can create a painting using one of the generated palettes. To notify the end of the painting, the user clicks on the DONE button. Once the button is pressed, the colors of the user's painting change as the facial expression of the user changes. Facial emotion recognition is used in this process to classify the emotional status of the user’s face.

Hierarchical Automated Machine Learning Approach for Self-Optimizable Driving Distraction Recognition Based on Driver’s Lane-Keeping Performance

With the enrichment of smartphone uses, phone-related driving distractions have become a threat to driving safety. One way to mitigate driving distractions is to detect them and provide real-time warnings. However, most existing driving distraction recognition algorithms are pretrained models composed of structures, hyperparameters, and parameters that may not be able to account for drivers’ individual differences and, thus, might result in low model accuracy. This study proposes a domain-specific hierarchical automated machine learning (HAT-ML) model that self-learns personalized optimal models to detect driving distractions from vehicle movement data. The HAT-ML model integrates key modeling steps into auto-optimizable layers, including knowledge-based feature extraction, feature selection by recursive feature elimination, automated algorithm selection, and hyperparameter autotuning by Bayesian optimization. In our eight-degrees-of-freedom driving simulator experiment, we demonstrated the effectiveness of the proposed model using three driving distraction tasks: browsing a short message, browsing a long message, and answering a phone call. The HAT-ML model was found to be reliable and robust for predicting phone-related driving distraction, achieving satisfactory results with a predictive accuracy of 80% at the group level and 90% at the individual level. Moreover, the results revealed that each distraction and driver type required different optimized hyperparameter values, which demonstrated the value of utilizing HAT-ML to detect driving distractions. The key elements that dominated the performance of the model have several theoretical and practical implications. The proposed method not only enhanced performance, but also provided data-driven insights about model development.

Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain.

This study aims to compare the variable selection strategies of different machine learning (ML) and statistical algorithms in the prognosis of neck pain (NP) recovery. A total of 3001 participants with NP were included. Three dichotomous outcomes of an improvement in NP, arm pain (AP), and disability at 3 months follow-up were used. Twenty-five variables (twenty-eight parameters) were included as predictors. There were more parameters than variables, as some categorical variables had >2 levels. Eight modelling techniques were compared: stepwise regression based on unadjusted p values (stepP), on adjusted p values (stepPAdj), on Akaike information criterion (stepAIC), best subset regression (BestSubset) least absolute shrinkage and selection operator [LASSO], Minimax concave penalty (MCP), model-based boosting (mboost), and multivariate adaptive regression splines (MuARS). The algorithm that selected the fewest predictors was stepPAdj (number of predictors, p = 4 to 8). MuARS was the algorithm with the second fewest predictors selected (p = 9 to 14). The predictor selected by all algorithms with the largest coefficient magnitude was "having undergone a neuroreflexotherapy intervention" for NP (β = from 1.987 to 2.296) and AP (β = from 2.639 to 3.554), and "Imaging findings: spinal stenosis" (β = from -1.331 to -1.763) for disability. Stepwise regression based on adjusted p-values resulted in the sparsest models, which enhanced clinical interpretability. MuARS appears to provide the optimal balance between model sparsity whilst retaining high predictive performance across outcomes. Different algorithms produced similar performances but resulted in a different number of variables selected. Rather than relying on any single algorithm, confidence in the variable selection may be increased by using multiple algorithms.

An algorithm for automatic selection and combination of forecast models

In this paper, we present an algorithm designed to automatically merge predictions from a collection of individual prediction methods coded in R. The algorithm employs varying weights and decision rules to ascertain the optimal amalgamation of these methods, with the aim of forecasting historical time series data while minimizing human intervention. The algorithm serves as an automated component within the artificial intelligence toolkit.The proposed algorithm (al), denoted as “alPCA” is founded on principal component analysis (PCA), hence the acronym. Commencing with 52 configurations of 11 distinct methods available in R, we calculate several loss functions: specifically, scaled Mean Absolute Percentage Error (sMAPE) and Mean Absolute Scaled Error (MASE) for both fitting (Training Phase) and prediction (Validation Phase), along with Root Mean Squared Error (RMSE) and Overall Weighted Average (OWA) solely for prediction (Validation Phase). We then employ PCA to reduce the error matrix derived from this data to one or two dimensions. Subsequently, the methods are ranked based on their proximity to the highest score. A probability distribution is fitted to this proximity metric, and utilizing the percentiles of these values, the optimal methods for combination are selected. We propose three categories of weights derived from the PCA scores, encompassing the fitting sMAPE (Training Phase) and the prediction sMAPE (Validation Phase), to facilitate the amalgamation process.This approach is applied to seven distinct univariate time series across diverse domains, including automobile sales, electricity production, and CO2 levels. Additionally, a set of 100 random monthly series from the M4 competition is included in the analysis. To assess the predictive precision of our algorithm, we compare its performance against three widely utilized combined prediction algorithms available in R. We evaluate the outcomes in Test Phase (unseen data) using four distinct loss functions and conduct a sensitivity analysis to gauge the algorithm's robustness and efficacy across various specifications.

Visual Coherence for Augmented Reality

Introduction. The 2020s were marked by the emergence of a new generation of computer simulators using augmented reality. One of the promising advantages of augmented reality technology is the ability to safely simulate hazardous situations real-world. A prerequisite for realizing this advantage is to provide the visual coherence of augmented reality scenes: virtual objects must be indistinguishable from real ones. All IT leaders consider augmented reality as a next “big wave”; thus, the visual coherence is becoming a key issue for IT in general. However, it is in aerospace applications that the visual coherence has already acquired practical significance. An example is Boeing's development of an augmented reality flight simulator, which began in 2022. Visual coherence is a complex problem, one of the aspects of which is to provide the correct overall coloration of virtual objects in an augmented reality scene. The objective of the research was to develop a new method of such tinting.Materials and Methods. The developed method (called spectral transplantation) uses two-dimensional spectral image transformations.Results. A spectral transplantation technology is proposed that provides direct transfer of color, brightness, and contrast characteristics from the real background to virtual objects. An algorithm for automatic selection of the optimal type of spectral transformation has been developed.Discussion and Conclusion. Being a fully automatic process without recording lighting conditions, spectral transplantation solves a number of complex problems of visual coherence. Spectral transplantation can be a valuable addition to other methods of providing visual coherence.

Optimal periodicity-enhanced group sparse for bearing incipient fault feature extraction

Efficient and automatic fault feature extraction of rotating machinery, especially for incipient faults is a challenging task of great significance. In this article, an optimal periodicity-enhanced group sparse method is proposed. Firstly, a period sequence determination method without any prior information is proposed, and the amplitude is calculated by the numerical characteristics of the vibration signal to obtain period square waves. Secondly, the periodic square waves are embedded into the group sparse algorithm, to eliminate the influence of random impulses, and intensify the periodicity of the acquisition signal. Thirdly, a fault feature indicator reflecting both signal periodicity and sparsity within and across groups is proposed as the fitness of the marine predator algorithm for parameter automatic selection. In addition, the method proposed is evaluated and compared by simulation and experiment. The results show that it can effectively extract incipient fault features and outperforms traditional overlapping group shrinkage and Fast Kurtogram.

An automatic band selection algorithm based on connection centre evolution

ABSTRACT The characteristics of high dimensionality, strong band correlation, and high information redundancy make band selection become a necessary step for efficient processing of hyperspectral images. In this paper, a new band selection method based on connection centre evolution (CCE) is proposed, named -nearest neighbour CCE band selection (NNCCEBS). First, according to CCE, we measure the band similarity under different observation scales by iteratively calculating the connectivity at different scales. Therefore, NNCCEBS can achieve multi-scale band selection, and is able to complete it at one time. Next, to avoid the selected bands being aggregated, an -nearest neighbour similarity matrix is introduced, which only retains the connectivity of -nearest bands for a band. By doing so, the new method is less sensitive to the parameter settings, and can select more accurate band centres. Experiments on real hyperspectral datasets prove the effectiveness of our method.

Noise-aware physics-informed machine learning for robust PDE discovery

This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finite observations but failed to maintain satisfying results against noisy data, partly owing to suboptimal estimated derivatives and found PDE coefficients. We address the issues by introducing a noise-aware physics-informed machine learning framework to discover the governing PDE from data following arbitrary distributions. We propose training a couple of neural networks, namely solver and preselector, in a multi-task learning paradigm, which yields important scores of basis candidates that constitute the hidden physical constraint. After they are jointly trained, the solver network estimates potential candidates, e.g. partial derivatives, for the sparse regression to initially unveil the most likely parsimonious PDE, decided according to information criterion. Denoising physics-informed neural networks, based on discrete Fourier transform, is proposed to deliver the optimal PDE coefficients respecting the noise-reduced variables. Extensive experiments on five canonical PDEs affirm that the proposed framework presents a robust and interpretable approach for PDE discovery, leading to a new automatic PDE selection algorithm established on minimization of the information criterion decay rate.

Technique for Automatic Algorithm Selection for Computational Intelligence in Cloud-Based Computing Environments

This research presents an Auto-Algorithm selection technique for Computational Intelligence in Cloud-Based computing environment. By leveraging the capabilities of a network information processing platform, users can effortlessly and intelligently build Computational Intelligence models tailored to their specific problems without the need for manually configuring the Computational Intelligence environment, selecting algorithms, or adjusting complex Computational Intelligence functions and parameters. The proposed procedure allows users to simply upload sample data through a web interface, freeing Computational Intelligence applications from environmental constraints and taking advantage of the network information processing platform's capabilities. This approach transparently handles the model building process, significantly reducing the barriers to entry for utilizing Computational Intelligence. By addressing the issues of unpredictable model selection, manual parameter adjustment, and the challenges faced by common users, this auto selection procedure empowers the practical application of Computational Intelligence in various domains.

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches

We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural networks to predict a packing heuristic in online bin-packing, selecting from four well-known heuristics. As input, the RNN methods only use the sequence of item-sizes. This contrasts to typical approaches to algorithm-selection which require a model to be trained using domain-specific instance features that need to be first derived from the input data. The RNN approaches are shown to be capable of achieving within 5% of the oracle performance on between 80.88 and 97.63% of the instances, depending on the dataset. They are also shown to outperform classical machine learning models trained using derived features. Finally, we hypothesise that the proposed methods perform well when the instances exhibit some implicit structure that results in discriminatory performance with respect to a set of heuristics. We test this hypothesis by generating fourteen new datasets with increasing levels of structure, and show that there is a critical threshold of structure required before algorithm-selection delivers benefit.

A novel density peaks clustering algorithm for automatic selection of clustering centers based on K-nearest neighbors.

The density peak clustering algorithm (DPC) requires manual determination of cluster centers, and poor performance on complex datasets with varying densities or non-convexity. Hence, a novel density peak clustering algorithm is proposed for the automatic selection of clustering centers based on K-nearest neighbors (AKDPC). First, the AKDPC classifies samples according to their mutual K-nearest neighbor values into core and non-core points. Second, the AKDPC uses the average distance of K nearest neighbors of a sample as its density. The smaller the average distance is, the higher the density. Subsequently, it selects the highest density sample among all unclassified core points as a center of the new cluster, and the core points that satisfy the merging condition are added to the cluster until no core points satisfy the condition. Afterwards, the above steps are repeated to complete the clustering of all core points. Lastly, the AKDPC labels the unclassified non-core points similar to the nearest points that have been classified. In addition, to prove the validity of AKDPC, experiments on manual and real datasets are conducted. By comparing the AKDPC with classical clustering algorithms and excellent DPC-variants, this paper demonstrates that AKDPC presents higher accuracy.

A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

The rapid development of mobile networks proliferates the demands of high data rate, low latency, and high-reliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multiple-input-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional cross-layer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a long-term system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios.

Cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ₁ -penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ₁ tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data.