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Explainable Rules and Heuristics in AI algorithm recommendation Approaches——A Systematic Literature Review and Mapping Study

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1023-1051页

作者： Francisco JoséGarcía-Penlvo Andrea Vázquez-Ingelmo Alicia García-Holgado GRIAL Research Group Computer Science DepartmentUniversity of SalamancaSalamanca37008Spain

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be *** AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very *** these reasons,it is important to rely on expert knowledge when applying these ***,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI *** goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input *** systematic review follows the methodology proposed by Kitchenham and other authors in the field of software *** a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were *** reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

关键词： SLR systematic literature review artificial intelligence machine learning algorithm recommendation heuristics explainability

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Real-Time algorithm recommendation Using Meta-Learning 13th

Real-Time Algorithm Recommendation Using Meta-Learning

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13th International Symposium on Ambient Intelligence

作者： Palumbo, Guilherme Guimaraes, Miguel Carneiro, Davide Novais, Paulo Alves, Victor Inst Politecn Porto CIICESI Escola Super Tecnol & Gestao Porto Portugal Univ Minho Ctr ALGORITMI Braga Portugal

ISBN: (纸本)9783031223556;9783031223563

As the field of Machine Learning evolves, the number of available learning algorithms and their parameters continues to grow. On the one hand, this is positive as it allows for the finding of potentially more accurate models. On the other hand, however, it also makes the process of finding the right model more complex, given the number of possible configurations. Traditionally, data scientists rely on trial-and-error or brute force procedures, which are costly, or on their own intuition or expertise, which is hard to acquire. In this paper we propose an approach for algorithm recommendation based on meta-learning. The approach can be used in real-time to predict the best n algorithms (based on a selected performance metric) and their configuration, for a given ML problem. We evaluate it through cross-validation, and by comparing it against an Auto ML approach, in terms of accuracy and time. Results show that the proposed approach recommends algorithms that are similar to those of traditional approaches, in terms of performance, in just a fraction of the time.

关键词： Machine learning Meta-learning algorithm recommendation

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Network Governance Prediction Based on Artificial Intelligence and algorithm recommendation

Network Governance Prediction Based on Artificial Intelligen...

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作者： Ruan, Xiaoying Chen, Hongfu Law School of Minjiang University Foreign Language College of Minjiang University FuzhouFujian350001 China

In recent years, China has entered the era of rapid development of network information, network communication mode is more diversified, professional and intelligent, leading to the governance of its communication order needs to face great challenges and development opportunities. As one of the ways of network communication, algorithmic recommendation can achieve accurate delivery to users and solve the problem of information flooding, but it also brings some problems. For example, some enterprises spread vulgar and harmful content in order to attract attention and search hot, which is a very severe challenge to the controllers of network transmission order. In this regard, this paper first introduces the algorithm recommendation, analyzes the problems faced by the network propagation of algorithm recommendation, and puts forward the corresponding countermeasures. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： algorithm recommendation Communication order Government Network

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RESEARCH ON DIGITAL MEDIA algorithm recommendation BASED ON SUPPORT VECTOR MACHINE

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 4836-4843页

作者： Lei, Mengwei Chen, Qiong Hunan Mass Media Vocat & Tech Coll Sch Visual Arts Changsha 410100 Hunan Peoples R China Cent South Univ Sch Mat Sci & Engn Changsha 410083 Hunan Peoples R China

Within digital media, the effectiveness of content material advice systems is pivotal for boosting user engagement and satisfaction. This study's article delves into the development and implementation of a singular set of rules recommendation gadgets based totally on the principles of support Vector device (SVM), a distinguished machine learning approach. The objective is to address the demanding situations faced by using traditional recommendation structures, such as media content problems, by leveraging the type and regression talents of SVM. The methodology encompasses the usage of a large dataset of person interactions and alternatives extracted from diverse virtual media systems. This statistic is then processed via an SVM version and relationships among user behaviors and content material characteristics. The particular issue of this technique lies in its adaptability and precision in dealing with excessive-dimensional facts, which is ordinary in digital media environments. The SVM model is high-quality-tuned to optimize content recommendation via not most effective matching consumer choices but additionally introducing a degree of content material variety to combat echo chambers. This research evaluates the performance of the SVMbased recommendation system towards traditional algorithms via a sequence of metrics inclusive of accuracy, range, and consumer engagement charges. This assessment gives insights into the efficacy of SVM in delivering extra applicable and various content to users, thereby enhancing their digital media experience.

关键词： Digital Media algorithm recommendation Support Vector Machine (SVM) Machine Learning Content recommendation Systems User Engagement Data Analysis High-dimensional Data Handling

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Dimensionality Reduction and Visualization in Public Management Research from the Perspective of algorithm recommendation

Dimensionality Reduction and Visualization in Public Managem...

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作者： Jun Yu Yichen Liu Economics and Management College Civil Aviation University of China School of Economics Henan University of Economics and Law

With the development of computer technology,algorithm recommendation systems have been widely used in intelligent transportation,urban planning,and other *** at the current problems in public management in China,this paper proposes a dimensionality reduction calculation method,and uses the improved particle swarm optimization algorithm to analyze and evaluate the dimensionality reduction *** the expectation of providing some reference value and ideas for solving practical application problems,a corresponding relationship equation is established with the minimum expected value as the objective function to solve the corresponding optimal solution,the evaluation results of system performance indicators and comprehensive scoring criteria under relevant parameters and constraints are *** test results show that the visualization effect of the dimensionality reduction visualization model for public management research based on the algorithm recommendation perspective is over 90%.

关键词： algorithm recommendation Public Management Dimensionality Reduction Visualization Dimensionality Reduction Analysis

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Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels

Feature selection algorithm recommendation for gene expressi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Aduviri, Robert Matos, Daniel Villanueva, Edwin Pontifical Catholic Univ Peru Lima Peru

ISBN: (纸本)9781538654880

Feature selection is an important step in gene expression data analysis. However, many feature selection methods exist and a costly experimentation is usually needed to determine the most suitable one for a given problem. This paper presents the application of gradient boosting and neural network techniques for the construction of metamodels that can recommend rankings of {feature selection - classification} algorithm pairs for new gene expression classification problems. Results in a corpus of 60 public data sets show the superiority of these techniques in producing more useful rankings in relation to classical metamodels.

关键词： Metalearning gene expression data feature selection cancer classification algorithm recommendation

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Dimensionality Reduction for the algorithm recommendation Problem

Dimensionality Reduction for the Algorithm Recommendation Pr...

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7th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Alcobaca, Edesio Mantovani, Rafael G. Rossi, Andre L. D. de Carvalho, Andre C. P. L. F. Univ Sao Paulo ICMC USP Inst Math & Comp Sci Sao Carlos SP Brazil Sao Paulo State Univ UNESP Campus Itapeva Sao Paulo Brazil

ISBN: (纸本)9781538680230

Given the increase in data generation, as many algorithms have become available in recent years, the algorithm recommendation problem has attracted increasing attention in Machine Learning. This problem has been addressed in the Machine Learning community as a learning task at the meta-level where the most suitable algorithm has to be recommended for a specific dataset. Since it is not trivial to define which characteristics are the most useful for a specific domain, several meta-features have been proposed and used, increasing the meta-data meta-feature dimension. This study investigates the influence of dimensionality reduction techniques on the quality of the algorithm recommendation process. Experiments were carried out with 15 algorithm recommendation problems from the Aslib library, 4 meta-learners, and 3 dimensionality reduction techniques. The experimental results showed that linear aggregation techniques, such as PCA and LDA, can be used in algorithm recommendation problems to reduce the number of meta-features and computational cost without losing predictive performance.

关键词： Dimensionality reduction algorithm recommendation Meta-learning

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The Role of recommendation algorithm in Online Social Platforms

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Procedia Computer Science 2025年 261卷 674-681页

作者： Yanmei Li Jiayi Cao School of Communication Qufu Normal University Rizhao China Faculty of Humanities and Social Sciences Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China

Since the 21st century, thanks to the emerging technologies such as artificial intelligence, recommendation algorithm, big data and cloud computing, some domestic and foreign online social platforms have developed and risen rapidly. With the support and promotion of these emerging technologies, domestic and foreign online social platforms are more intelligent and convenient, the number of users is increasing, and the influence of the platforms is expending. This article regard Weibo, TikTok, Facebook and Twitter as the main research objects, discuss the role of recommendation algorithm in online social platforms. Through the research and analysis, the characteristics of the application of recommendation algorithm in online social platforms are summarized.

关键词： Big data online social platforms algorithm recommendation new media

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Feature Extraction for recommendation of Constrained Multiobjective Evolutionary algorithms

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第4期27卷 949-963页

作者： Qiao, Kangjia Yu, Kunjie Qu, Boyang Liang, Jing Yue, Caitong Ban, Xuanxuan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China

The evolutionary algorithm recommendation is catching increasing attention when solving practical application problems since different algorithms often perform differently on different problems. To achieve the algorithm recommendation, extracting effective features to accurately characterize the problems is necessary, which is related to the feature extraction problem. So far, most feature extraction methods focus on single-objective optimization problems, and only a few studies are conducted on multiobjective optimization problems and constrained optimization problems, let alone constrained multiobjective optimization problems (CMOPs) that are widely encountered in the real world. To fill the gap, this article proposes an evolution-based constrained multiobjective feature extraction method (ECMOFE), in which the information generated in the evolutionary process is leveraged to form the feature matrix. To be specific, we create two populations to, respectively, optimize constraints and objectives for some generations. Furthermore, two complementary evolutionary operators are used to generate offspring for each population. In the environmental selection, the successful rate of offspring individuals generated by each operator of each population is recorded to form the feature matrix. Then, a dimension reduction method is designed to compress the size of the feature matrix. By the above process, the feature vector that can reflect the global relationship between constraints and objectives and the difficulty of the CMOP is formed. Based on the formed features, several algorithm recommendation methods are built on the basis of classifiers. The results based on multiple metrics show the effectiveness of the proposed ECMOFE.

关键词： algorithm recommendation constrained multiobjective optimization feature extraction

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The influence of recommendation algorithm's information flow on targeted advertising audience's coping behavior

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ACTA PSYCHOLOGICA 2024年 243卷 104168页

作者： Chen, Lijun Sun, Rui Yuan, Yuan Zhan, Xuemei Huaqiao Univ Sch Business Adm Quanzhou 362021 Peoples R China Jimei Univ Sch Business Adm Xiamen 361021 Peoples R China

Targeted advertising equipped with a recommendation algorithm can achieve accurate matching between users and recommended content, but overly precise recommendations may exacerbate negative audience reactions or behaviors. Improving the transparency of algorithm recommendation is one of the ways to address audience concerns or skepticism, and transparency guarantees the audience's right to know and thus brings more trust, which will reduce the audience's negative behavior. But increased transparency may also make the audience feel pressured or threatened, and requiring more cognitive and behavioral effort, which was called coping behavior. In order to clarify the relationship between the transparency of the algorithm recommendation and the audience's coping behavior, based on the persuasion theory, this study discusses the mechanism of the influence of the characteristics of the algorithm recommendation information flow on the audience's coping behavior of targeted advertising from the perspective of the flow mode and transmission principle of information. Based on the data of 120 online pretests and 297 formal tests, the results show that the perceived trust and perceived threat caused by the information flow characteristics of the algorithm recommendation jointly determine the possible coping behaviors of targeted advertising audiences. Additionally, users' self-efficacy regulates the relationship between mental process and coping behavior. Different from previous studies on audience coping behaviors of targeted ads, which mainly start from the perspective of participants and advertising content, this research tries to start from the perspective of information flow. The research results demystify the relationship between recommendation algorithm information flow and the audience's coping behavior, and enrich the algorithmic persuasion framework. The research results have reference value for the improvement of personalized recommendation effect, and provide a new w

关键词： algorithm recommendation Information flow Targeted advertising Coping behavior Self-efficacy

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