Towards a Unified Model Representation of Machine Learning Knowledge

Martínez-Rojas, Antonio; Ramirez, Andres J.; Enríquez, J. G.

doi:10.5220/0008559204700476

Cited by 2 publications

(11 citation statements)

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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.…”

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

confidence: 99%

“…The remaining work in this category combines a review of cheat sheets with expert knowledge to build a common language for ML algorithm recommendation [29], however, details about the implementation are undisclosed.…”

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%

“…AI algorithm selection approaches could employ different aspects that influence the recommendation process. In this case, from the nine selected papers, we identified two main factors: dataset characteristics (present in every selected work) but also the task to be carried out through AI, present in [29,32], or the algorithm requirements and characteristics [28,31]. Table 2 summarizes the results.…”

Section: Mq4 Which Are the Factors That Condition The Algorithm Recom...mentioning

confidence: 99%

“…Only one of the papers has not been explicitly tested regarding its functionality, as they present a theoretical proposal for the unification of ML knowledge [29]. The remaining papers have tested their solutions whether in terms of performance or in terms of functionality.…”

Section: Mq7 How Many Studies Have Tested Their Proposed Solutions?mentioning

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: Mq4 Which Are the Factors That Condition The Algorithm Recom...mentioning

confidence: 99%

Section: Mq7 How Many Studies Have Tested Their Proposed Solutions?mentioning

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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A Unified Model Representation of Machine Learning Knowledge

Enríquez

Martínez-Rojas

Lizcano

et al. 2020

JWE

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Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios. However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things. Since there exist thousands of algorithms, it becomes a time-consuming and challenging task. To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process. However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results. To make a smarter and more efficient selection, a repository of knowledge is necessary. To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on, and (3) a traceability links maintenance. The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct.

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Towards a Unified Model Representation of Machine Learning Knowledge

Cited by 2 publications

References 0 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

A Unified Model Representation of Machine Learning Knowledge

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