Learning by Arguing in Argument-Based Machine Learning Framework

Guid, Matej; Možina, Martin; Pavlič, Matevž; Turšič, Klemen

doi:10.1007/978-3-030-22244-4_15

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ABCN2 does not automatically generate suggestions for new features. ABCN2 was used in an intelligent tutoring system (Woolf, 2010) to teach students arguing strategies (Guid et al, 2019). The intelligent system by Xing et al (2022b) uses feature extraction in semi-supervised learning and self-distillation; a similar feature extractor was used in the federated distillation learning system (Xing et al, 2022a).…”

Section: Standalone Feature Constructionmentioning

confidence: 99%

Feature construction using explanations of individual predictions

Vouk,

Guid,

Robnik-Šikonja

2023

Preprint

View full text Add to dashboard Cite

Feature construction can contribute to comprehensibility and performance of machine learning models. Unfortunately, it usually requires exhaustive search in the attribute space or time-consuming human involvement to generate meaningful features. We propose a novel heuristic approach for reducing the search space based on aggregation of instance-based explanations of predictive models. The proposed Explainable Feature Construction (EFC) methodology identifies groups of cooccurring attributes exposed by popular explanation methods, such as IME and SHAP. We empirically show that reducing the search to these groups significantly reduces the time of feature construction using logical, relational, Cartesian, numerical, and threshold (num-of-N and X-of-N) constructive operators. An analysis on 10 transparent synthetic datasets shows that EFC effectively identifies informative groups of attributes and constructs relevant features. Using 30 real-world classification datasets, we show significant improvements in classification accuracy for several classifiers and demonstrate the feasibility of the proposed feature construction even for large datasets. Finally, EFC generated interpretable features on a real-world problem from the financial industry, which were confirmed by a domain expert.

show abstract

Section: Standalone Feature Constructionmentioning

confidence: 99%

Feature construction using explanations of individual predictions

Vouk,

Guid,

Robnik-Šikonja

2023

Preprint

View full text Add to dashboard Cite

show abstract

Feature construction using explanations of individual predictions

Vouk

Guid

Robnik–Šikonja

2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

ML-Based Teaching Systems: A Conceptual Framework

Spitzer,

Kühl,

Heinz

et al. 2023

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and pass it on to novices. While this knowledge transfer has traditionally occurred through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to understand better and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda.

show abstract

Learning by Arguing in Argument-Based Machine Learning Framework

Cited by 3 publications

References 10 publications

Feature construction using explanations of individual predictions

Feature construction using explanations of individual predictions

Feature construction using explanations of individual predictions

ML-Based Teaching Systems: A Conceptual Framework

Contact Info

Product

Resources

About