Search

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. [ABSTRACT FROM AUTHOR]

The extensive application of algorithm recommendation technology not only meets the information needs of network users but also leads to the emergence of "information cocoons". On the basis of summarizing three generating mechanisms, namely, the theory of technological innovation, the theory of interest-driven, and the theory of emotional identity, this paper constructs a game model of bilateral evolution between information platforms and network users and simulates the influence path of key factors on the evolution of both parties' main strategies. The research shows that algorithm recommendation technology is the root of "information cocoons" in the algorithm era. As the algorithm technology matures day by day, the cost of using algorithm recommendations on information platforms and the loss cost of accepting algorithm recommendations by network users are constantly decreasing, which causes the information platforms and network users' strategy choice for algorithm recommendation to evolve from {give up and conflict} to {use and accept}, and finally leads to the long-term existence of "information cocoons". [ABSTRACT FROM AUTHOR]

The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means 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 dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AI-expert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The 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 features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms. [ABSTRACT FROM AUTHOR]

Under the wave of globalized digital news, news value changes due to the influence of complex economic factors, organizational factors, social factors, and cultural factors in the news ecological environment. Through text mining and discourse analysis, this study found that the classic news value standard has been reconstructed in the digital age, and the algorithm-driven platform re-adjusted the traditional news value standard, and established a standard model of user, content, and technical algorithm value led by algorithm engineers and platform managers. It reveals the pseudo-neutrality of algorithm technology and the subjectivity of the people behind the algorithm. At the same time, based on the in-depth analysis of the discourse strategy and discourse framework behind the public speech, it is revealed that this new communication carrier makes news based on the discourse strategy of subcultural symbols, the leverage of others, self-authority and the discourse framework of “individual-society-state”. The material attribute of aggregation platform is endowed with subjective social attributes, in order to establish value measurement requirements different from traditional news value elements and gain social recognition. [ABSTRACT FROM AUTHOR]

Algorithm recommendation based on meta-learning was studied previously. The research on the meta-features extraction, which is a key for the success of recommendation, is lacking for multi-objective optimization problems (MOPs). This paper proposes four sets of meta-features to characterize MOPs. In addition, the algorithm recommendation model based on meta-learning is extended to the field of multi-objective optimization. To evaluate the efficiency and effectiveness of the extracted meta-features, 29 MOPs benchmark functions with different dimensions and two real-world MOPs are employed for comprehensive comparison. Experimental results show that the proposed meta-features in this paper can fully characterize MOPs and are empirically efficient for algorithm recommendation. [ABSTRACT FROM AUTHOR]

The extensive application of algorithm recommendation technology not only meets the information needs of network users but also leads to the emergence of “information cocoons”. On the basis of summarizing three generating mechanisms, namely, the theory of technological innovation, the theory of interest-driven, and the theory of emotional identity, this paper constructs a game model of bilateral evolution between information platforms and network users and simulates the influence path of key factors on the evolution of both parties’ main strategies. The research shows that algorithm recommendation technology is the root of “information cocoons” in the algorithm era. As the algorithm technology matures day by day, the cost of using algorithm recommendations on information platforms and the loss cost of accepting algorithm recommendations by network users are constantly decreasing, which causes the information platforms and network users’ strategy choice for algorithm recommendation to evolve from {give up and conflict} to {use and accept}, and finally leads to the long-term existence of “information cocoons”.

This paper presents an experimental comparison among four automated machine learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on evolutionary algorithms (EAs), and the other is Auto-WEKA, a well-known AutoML method based on the combined algorithm selection and hyper-parameter optimisation (CASH) approach. The EA-based methods build classification algorithms from a single machine learning paradigm: either decision-tree induction, rule induction, or Bayesian network classification. Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms. We performed controlled experiments where these four AutoML methods were given the same runtime limit for different values of this limit. In general, the difference in predictive accuracy of the three best AutoML methods was not statistically significant. However, the EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA. We also observed that Auto-WEKA has shown meta-overfitting, a form of overfitting at the meta-learning level, rather than at the base-learning level. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Humanities & Social Sciences is the property of Journal of Shenzhen University (Humanities & Social Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In personalized medicine, a challenging task is to identify the most effective treatment for a patient. In oncology, several computational models have been developed to predict the response of drugs to therapy. However, the performance of these models depends on multiple factors. This paper presents a new approach, called Q-Rank, to predict the sensitivity of cell lines to anti-cancer drugs. Q-Rank integrates different prediction algorithms and identifies a suitable algorithm for a given application. Q-Rank is based on reinforcement learning methods to rank prediction algorithms on the basis of relevant features (e.g., omics characterization). The best-ranked algorithm is recommended and used to predict the response of drugs to therapy. Our experimental results indicate that Q-Rank outperforms the integrated models in predicting the sensitivity of cell lines to different drugs. [ABSTRACT FROM AUTHOR]

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 way to further study the transparency of algorithm recommendation in the field of consumer behavior. Meanwhile, it also provides suggestions for the practices of platforms and advertisers in practice. [ABSTRACT FROM AUTHOR]

• Automated selection of image segmentation algorithms. • Computational cost reduction in image segmentation tasks. • Meta-learning outperformed any single algorithm explored over the whole dataset. • Machine Learning and Gradient-based image segmentation algorithms were studied. • Experiments were conducted over a widely known image dataset with 2100 examples. Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance. [ABSTRACT FROM AUTHOR]

Intelligent algorithm recommendation is an important branch of the research field of hyper¬heuristic algorithms. Its goal is to automatically select the most suitable algorithm for the problem to be solved from many"online" algorithms, thereby greatly improving the efficiency of problem solving. We propose and validate an intelligent optimization algorithm recommendation system, whose theoretical ba¬sis is the No Free Lunch theorem and Rice?s algorithm selection framework. It assumes that there is a potential correlation between problem features and algorithm performance, thus the algorithm recom-mendation problem can be converted into a multi-classification problem. In order to verify the assump-tion,the multi-mode resource constrained project scheduling problem is chosen as the test sample data, a number of meta-heuristic optimization algorithms such as the particle swarm optimization, simulated annealing, tabu search, and artificial bee swarm, are used as the recommended algorithms, and the multi-classification strategy of support vector machine is applied to achieve algorithm classification rec¬ommendation. Cross-validation results show that the recommendation accuracy exceeds 90* and the e¬ valuation indicators perform well. [ABSTRACT FROM AUTHOR]

In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

Objective Manual evaluation of machine learning algorithms and selection of a suitable classifier from the list of available candidate classifiers, is highly time consuming and challenging task. If the selection is not carefully and accurately done, the resulting classification model will not be able to produce the expected performance results. In this study, we present an accurate multi-criteria decision making methodology (AMD) which empirically evaluates and ranks classifiers’ and allow end users or experts to choose the top ranked classifier for their applications to learn and build classification models for them. Methods and material Existing classifiers performance analysis and recommendation methodologies lack (a) appropriate method for suitable evaluation criteria selection, (b) relative consistent weighting mechanism, (c) fitness assessment of the classifiers’ performances, and (d) satisfaction of various constraints during the analysis process. To assist machine learning practitioners in the selection of suitable classifier(s), AMD methodology is proposed that presents an expert group-based criteria selection method, relative consistent weighting scheme, a new ranking method, called optimum performance ranking criteria, based on multiple evaluation metrics, statistical significance and fitness assessment functions, and implicit and explicit constraints satisfaction at the time of analysis. For ranking the classifiers performance, the proposed ranking method integrates Wgt.Avg.F-score, CPUTimeTesting, CPUTimeTraining, and Consistency measures using the technique for order performance by similarity to ideal solution (TOPSIS). The final relative closeness score produced by TOPSIS, is ranked and the practitioners select the best performance (top-ranked) classifier for their problems in-hand. Findings Based on the extensive experiments performed on 15 publically available UCI and OpenML datasets using 35 classification algorithms from heterogeneous families of classifiers, an average Spearman's rank correlation coefficient of 0.98 is observed. Similarly, the AMD method has showed improved performance of 0.98 average Spearman's rank correlation coefficient as compared to 0.83 and 0.045 correlation coefficient of the state-of-the-art ranking methods, performance of algorithms (PAlg) and adjusted ratio of ratio (ARR). Conclusion and implication The evaluation, empirical analysis of results and comparison with state-of-the-art methods demonstrate the feasibility of AMD methodology, especially the selection and weighting of right evaluation criteria, accurate ranking and selection of optimum performance classifier(s) for the user's application's data in hand. AMD reduces expert's time and efforts and improves system performance by designing suitable classifier recommended by AMD methodology. [ABSTRACT FROM AUTHOR]