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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Misir, Mustafa Istinye Univ Dept Comp Engn Istanbul Turkey Duke Kunshan Univ Div Nat & Appl Sci Kunshan Peoples R China

ISBN: (纸本)9781728190488

The present work applies algorithm selection for automatically determining the best energy functions for search algorithms on Protein Structure Prediction. Protein Structure Prediction is a critical problem concerned with exploring the structure of a protein, given an amino acid sequence. For the sake of improved search performance, various models have been introduced such as the Hydrophobic Polar (HP) models. These models make PSP computationally approachable. There exists a large suite of algorithms introduces to solve PSP. As in all other problem domains, it is possible to see that the PSP algorithms considered to be strong can fail to address some problem instances. One way of providing further performance improvements without devising a new algorithm is to automatically determine the best possible algorithm for a given PSP problem instance. algorithm selection focuses on automatically choosing algorithms for any given problem solving scenario. Unlike the traditional use of AS, this study accommodates algorithm selection for specifying the best suited energy function used for search. The process is designed as a rank prediction task on 7 energy functions for Iterated Local Search as the optimization algorithm. The idea is to assess the best energy function that can guide the search process of Iterated Local Search on a per-instance basis. An experimental analysis is reported on 30 PSP instances, each half belongs to the 2D and 3D HP models, respectively. The results show that algorithm selection is capable to offer proper selection of the energy functions with robustness. Additionally, a brief algorithm and instance analysis is reported on the instance hardness and matching algorithmic capabilities. This analysis further provides insights on the instance hardness besides instance and algorithm similarities.

关键词： algorithm selection Protein Structure Prediction Enegry Function 2D HP Model 3D HP Model

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Predicting Employee Voluntary Departure: Leveraging Machine Learning algorithms for Improved Retention Strategies 2

Predicting Employee Voluntary Departure: Leveraging Machine ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sarmento, Gonçalo Mota, Mickael Abbasi, Maryam Martins, Pedro Department of Informatics Polytechnic of Viseu Viseu Portugal Polytechnic Institute of Coimbra Applied Research Institute Coimbra Portugal Research Centre in Digital Services Polytechnic of Viseu Viseu Portugal

ISBN: (纸本)9798350305418

Employee voluntary departure presents significant challenges for organizations, affecting productivity and incurring substantial costs related to recruitment and training. This study aims to tackle this crucial issue by utilizing a comprehensive human resources dataset, which includes 14,999 records across ten columns. Its objective is to predict whether an employee will remain with the company or leave. To achieve this, seven distinct machine learning algorithms were employed: Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machines, Logistic Regression, and Multi-Layer Perceptron. The performance of each algorithm was assessed through a series of experiments involving different percentages of test data and variations in dataset processing. Notably, the Random Forest classifier emerged as the most accurate model, achieving an impressive accuracy rate of 98.47%. © 2023 IEEE.

关键词： algorithm selection Comparative analysis Human resources Machine learning algorithms Performance evaluation Predictive modeling

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How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank algorithms?

How Far Out of Distribution Can We Go With ELA Features and ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Petelin, Gasper Cenikj, Gjorgjina Jožef Stefan Institute Jožef Stefan International Postgraduate School Computer Systems Department Ljubljana Slovenia

ISBN: (纸本)9781665430654

algorithm selection is a critical aspect of continuous black-box optimization, and various methods have been proposed to choose the most appropriate algorithm for a given problem. One commonly used approach involves employing Exploratory Landscape Analysis (ELA) features to represent optimization functions and training a machine-learning meta-model to perform algorithm selections based on these features. However, many meta-models trained on existing benchmarks suffer from limited generalizability. When faced with a new optimization function, these meta-models often struggle to select the most suitable algorithm, restricting their practical application. In this study, we investigate the generalizability of meta-models when tested on previously unseen functions that were not observed during training. Specifically, we train a meta-model on base COmparing Continuous Optimizers (COCO) functions and evaluate its performance on new functions derived as affine combinations between pairs of the base functions. Our findings demonstrate that the task of ranking algorithms becomes substantially more challenging when the functions differ from those encountered during meta-learning training. This indicates that the effectiveness of algorithm selection diminishes when confronted with problem instances that substantially deviate from the training distribution. In such scenarios, meta-models that use ELA features to predict algorithm ranks do not outperform mere predictions of the average algorithm ranks. © 2023 IEEE.

关键词： algorithm selection black-box optimization evolutionary computation exploratory landscape analysis

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AutoClass: AutoML for Data Stream Classification

AutoClass: AutoML for Data Stream Classification

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Bahri, Maroua Georgantas, Nikolaos Inria Paris Paris France

ISBN: (纸本)9798350324457

Automated Machine Learning (autoML) is a novel topic that aims to tackle the parameter configuration issue using automatic monitoring models and comprises different machine learning tasks, such as feature selection, model selection, and hyper-parameter tuning. It makes easier use of algorithms for non-ML experts as well as ML experts by automating tasks that rely on expert domain knowledge. Nevertheless, autoML is in its infancy stage and not well explored yet in the offline and stream settings. In this paper, we propose automated Classification (auto-Class) method for automated algorithm selection and configuration for data stream classification. AutoClass consists of training an ensemble of different tuned configurations and selecting the best-performing configuration to do the prediction. We present experiments performed on a diverse set of real and artificial datasets and show how our proposed approach can outperform the performance of competitive state-of-the-art ensemble and single-based methods. © 2023 IEEE.

关键词： algorithm selection AutoML data stream hyper-parameter tuning stream classification

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AHP based Classification algorithm selection for Clinical Decision Support System Development

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Procedia Computer Science 2014年 36卷 328-334页

作者： Sina Khanmohammadi Mandana Rezaeiahari Department of Systems Science and Industrial Engineering Watson School of Engineering State University of New York at Binghamton Binghamton New York 13902-6000 U.S.A

Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use different algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based meta-learning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS). The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets.

关键词： Clinical Decision Support System (CDSS) Machine Learning Medical Informatics algorithm selection AHP Meta-Learning

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A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Cenikj, Gjorgjina Petelin, Gasper Eftimov, Tome Jozef Stefan Inst Comp Syst Dept Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia Jamova Cesta 39 Ljubljana 1000 Slovenia

The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models' generalizability in single -objective numerical optimization across diverse benchmark suites. Using Exploratory Landscape Analysis (ELA) and transformer -based (TransOpt) features, the research investigates their individual and combined effectiveness in AS across four benchmarks: BBOB, AFFINE, RANDOM, and ZIGZAG. AS models perform differently based on benchmark suite similarities in algorithm performance distributions and single -best solvers. When suites align, these models underperform against a baseline predicting mean algorithm performance;yet, they outperform the baseline when suites differ in performance distributions and solvers. The AS models trained using the ELA landscape features are better than the models trained using the TransOpt features on the BBOB and AFFINE benchmark suites, while the opposite is true for the RANDOM benchmark suite. Ultimately, the study reveals challenges in accurately capturing algorithm performance through problem landscape features (ELA or TransOpt), impacting AS model applicability.

关键词： algorithm selection Multi-target regression Generalization Benchmarking

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Robust Deep Reinforcement Learning algorithm for VNF-FG Embedding 47

Robust Deep Reinforcement Learning Algorithm for VNF-FG Embe...

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47th IEEE Conference on Local Computer Networks (LCN)

作者： Bouroudi, Abdelmounaim Outtagarts, Abdelkader Hadjadj-Aoul, Yassine Nokia Bell Labs Nokia Paris Saclay Nozay France Univ Rennes INRIA CNRS IRISA Rennes France

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Network slicing, also known as the virtual network embedding (VNE) problem, is an NPhard optimization problem. Compared to traditional approaches, the methods relying on deep reinforcement learning yield better performance without exhibiting issues such as stacking at local minima and/or solutions' space exploration limits. These algorithms present, however, different performances according to the employed approach, and the problem to be treated, resulting in robustness problems. To overcome these limits, we propose the adoption of the best algorithm, from a selection of learning strategies, in terms of reward and sample efficiency at each time step. The proposed strategy acts as a meta-algorithm that brings more robustness to the network by dynamically selecting the best solution for a specific scenario. Our solution proved its efficiency and managed to dynamically select the best algorithm in terms of the best acceptance ratio of the deployed services and outperform all the stand-alone algorithms.

关键词： Virtual Network Embedding Reinforcement Learning algorithm selection B5G/6G

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selection-based Per-Instance Heuristic Generation for Protein Structure Prediction of 2D HP Model

Selection-based Per-Instance Heuristic Generation for Protei...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Misir, Mustafa Istinye Univ Dept Comp Engn Istanbul Turkey Duke Kunshan Univ Div Nat & Appl Sci Kunshan Peoples R China

ISBN: (纸本)9781728190488

The present study aims at generating heuristics for Protein Structure Prediction represented in the 2D HP model. Protein Structure Prediction is about determining the 3-dimensional form of a protein from a given amino acid sequence. The resulting structure directly relates to the functionalities of the protein. There are a wide range of algorithms to address Protein Structure Prediction as an optimization problem. Being said that there is no an ultimate algorithm that can effectively solve PSP under varying experimental settings. Hyper-heuristics can offer a solution as high-level, problem-independent search and optimization strategies. selection Hyper-heuristics operate on given heuristic sets that directly work on the solution space. One group of selection Hyper-heuristics focus on automatically specify the best heuristics on-the-fly. Yet, the candidate heuristics tend to be decided, preferably a domain expert. Generation Hyper-heuristics approach differently as aiming to generate such heuristics automatically. This work introduces a automated heuristic generation strategy supporting selection Hyper-heuristics. The generation task is formulated as a selection problem, disclosing the best expected heuristic specifically for a given problem instance. The heuristic generation process is established as a parameter configuration problem. The corresponding system is devised by initially generating a training data alongside with a set of basic features characterizing the Protein Structure Prediction problem instances. The data is generated discretizing the parameter configuration space of a single heuristic. The resulting data is used to predict the best configuration of a specific heuristic used in a heuristic set under selection Hyper-heuristics. The prediction is performed separately for each instance rather than using one setting for all the instances. The empirical analysis showed that the proposed idea offers both better and robust performance on 22 PSP instances c

关键词： algorithm selection selection Hyper-heuristics Heuristic Generation Protein Structure Prediction 2D HP Model

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Selecting, optimizing and externally validating a preexisting machine-learning regression algorithm for estimating waist circumference

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 169卷 107909-107909页

作者： Phillips-Farfan, Bryan V. Inst Nacl Pediat Lab Nutr Expt Alcaldia Coyoacan Insurgentes Sur 3700Letra C Mexico City 04530 Mexico

Obesity, typically defined by the body mass index (BMI), has well known negative health effects. However, the BMI has serious deficiencies in predicting the adverse risks associated to obesity. Waist circumference (WC) is an alternative to define obesity and a better disease predictor according to the literature. However, old databases often lack this information, it is inaccurate (collected via self-report) or it is incomplete. Thus, this study accurately assesses WC using machine learning. The novel approaches are: 1) predictor variables (weight, height, age and sex) likely to appear in most data sets are used. 2) Publicly available data (including non-adults) and algorithms are used. 3) Systematic methods for data cleanup, model selection, hyperparameter optimization and external validation are performed. Data are cleaned: one variable per column, no special codes, missing values or outliers. Preexisting regression algorithms are gaged by cross-validation, using one data set. The hyperparameters of the best performing algorithm are optimized. The tuned algorithm is externally validated with other data sets by cross-validation. In spite of the limited number of features, the tuned algorithm outperforms prior WC approximations, using the same or similar predictor variables. The tuned algorithm enables using data where WC is not measured, is incomplete or is unreliable. A similar approach would be useful to estimate other variables of interest.

关键词： Machine-learning algorithm selection Hyperparameter optimization Model inspection External cross-validation Regression

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3D meta-classification: A meta-learning approach for selecting 3D point-cloud classification algorithm

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INFORMATION SCIENCES 2024年 662卷

作者： Xu, Fan Chen, Jun Shi, Yizhou Ruan, Tianchen Wu, Qihui Zhang, Xiaofei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

algorithm selection technology, aimed at choosing the most suitable algorithm for a given machine learning task, has achieved significant success in the domain of 2D vision. However, few studies have explored its application to the 3D point cloud domain. The rapid proliferation and development of 3D point cloud classification algorithms underscore the urgent need for exploration in selecting these algorithms. In this paper, we propose a novel meta -learningbased 3D classification approach, termed 3D meta -classification, to address this gap. The approach operates at both the base -level and meta -level phases. At the base level, candidate 3D classification algorithms constantly classify various 3D datasets, recording their classification performance as empirical knowledge. As for the meta -level phase, it specifically tailors the 3D meta -knowledge generator, 3D meta -feature extractor, and 3D meta -database constructor to establish the 3D meta -database, capturing the relationship between the 3D meta -features and empirical knowledge. Leveraging the 3D meta -database, the 3D classification meta -learner trains the meta -model and predicts suitable algorithms for new incoming 3D datasets. Extensive experiments are conducted to select the best -performing classification algorithm for specific 3D datasets from ModelNet40. The results demonstrate the effectiveness of the proposed 3D metaclassification model;the accuracies of one -from -two and one -from -three algorithm selection tasks reach over 98% and 80%, respectively.

关键词： Meta-learning 3D point-cloud classification algorithm selection 3D meta-features 3D classification meta-learner

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