An Ontological Approach for Recommending a Feature Selection Algorithm

Nayak, Aparna; Božić, Bojan; Longo, Luca

doi:10.1007/978-3-031-09917-5_20

Cited by 3 publications

(9 citation statements)

References 37 publications

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“…The second methodology involves literature reviews and analysis of the application of AI algorithms to different context to obtain a set of structured guidelines and heuristics [28,29,31,32]. Finally, authors in [33] created an ontology to support the algorithm recommendation process. Table 3 shows the results of this mapping question.…”

Section: Mq5 Which Methods Have Been Used For Algorithm Recommendation?mentioning

confidence: 99%

“…Finally, an ontology-based approach was employed in [33], complemented with Semantic Web Rule Language (SWRL) rules to support the recommendation of AI algorithms based on the relationships of the ontology's entities.…”

Section: Rq1 Which Methods Have Been Applied To Support Ai Selection ...mentioning

confidence: 99%

“…3. From the nine selected papers, two of them belong to the early 2000s period [25,26], while the remaining belong to the last six years interval [27][28][29][30][31][32][33].…”

Section: Mq1 How Many Studies Were Published Over the Years?mentioning

confidence: 99%

“…5, the target AI algorithms of the recommendation process are mostly framed in the "classification" category [25,26,28,[30][31][32]. But there are some papers that also provide heuristics for clustering [28], regression [28,32], and feature selection problems [27,33]. In addition, authors in [31] provided heuristics for an important task within AI pipelines; the encoding of categorical variables during the preprocessing tasks.…”

Section: Mq6 Which Ai Problems Are Tackled?mentioning

confidence: 99%

“…The above dataset characteristics are the meta-features of their respective meta-learning approaches. However, similar metrics are used in the ontology-based approach presented in [33], where authors employ a reduced set of simple, statistical and information theoretical values to characterize the datasets (Table 10). In the case of works that have captured heuristics from literature reviews, the data measurements vary.…”

Section: Dispersion Of the Data Setmentioning

confidence: 99%

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Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

Jos�Garc韆-Pe馻lvo¹,

V醶quez-Ingelmo²,

Garc韆-Holgado³

2023

Computer Modeling in Engineering &Amp; Sciences

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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-AIexpert 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.

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Section: Mq5 Which Methods Have Been Used For Algorithm Recommendation?mentioning

confidence: 99%

Section: Rq1 Which Methods Have Been Applied To Support Ai Selection ...mentioning

confidence: 99%

“…3. From the nine selected papers, two of them belong to the early 2000s period [25,26], while the remaining belong to the last six years interval [27][28][29][30][31][32][33].…”

Section: Mq1 How Many Studies Were Published Over the Years?mentioning

confidence: 99%

Section: Mq6 Which Ai Problems Are Tackled?mentioning

confidence: 99%

Section: Dispersion Of the Data Setmentioning

confidence: 99%

See 3 more Smart Citations

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

Jos�Garc韆-Pe馻lvo¹,

V醶quez-Ingelmo²,

Garc韆-Holgado³

2023

Computer Modeling in Engineering &Amp; Sciences

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Flexible Heuristics for Supporting Recommendations Within an AI Platform Aimed at Non-expert Users

Vázquez-Ingelmo

García‐Holgado

García-Peñalvo

et al. 2023

Lecture Notes in Computer Science

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The use of Machine Learning (ML) to resolve complex tasks has become popular in several contexts. While these approaches are very effective and have many related benefits, they are still very tricky for the general audience. In this sense, expert knowledge is crucial to apply ML algorithms properly and to avoid potential issues. However, in some situations, it is not possible to rely on experts to guide the development of ML pipelines. To tackle this issue, we present an approach to provide customized heuristics and recommendations through a graphical platform to build ML pipelines, namely KoopaML, focused on the medical domain. With this approach, we aim not only at providing an easy way to apply ML for non-expert users, but also at providing a learning experience for them to understand how these methods work.

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Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach

Nayak

Božić

Longo

2023

JWE

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Feature selection plays an important role in machine learning and data mining problems. Identifying the best feature selection algorithm that helps to remove irrelevant and redundant features is a complex task. This research tries to address it by recommending a feature selection algorithm based on dataset meta-features. The main contribution of the work is the use of Semantic Web principles to develop a recommendation model for the feature selection algorithm. As a result, dataset meta-features are modeled in a domain ontology, and a set of Semantic Web rule language (SWRL) predictive rules have been proposed to recommend a feature selection algorithm. The result of this research is a feature selection algorithm recommendation based on the data characteristics and quality (FSDCQ) ontology, which not only helps with recommendations but also finds the data points with data quality violations. An experiment is conducted on the classification datasets from the UCI repository to evaluate the proposed ontology. The usefulness and effectiveness of the proposed method is evaluated by comparing it with the widely used method in the literature for the recommendation. Results show that the ontology-based recommendations are equally good as the widely used recommendation model, which is k-NN, with added benefits.

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An Ontological Approach for Recommending a Feature Selection Algorithm

Cited by 3 publications

References 37 publications

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

Flexible Heuristics for Supporting Recommendations Within an AI Platform Aimed at Non-expert Users

Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach

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