150 Successful Machine Learning Models

Bernardi, Lucas; Mavridis, Themistoklis; Estévez, P. A.

doi:10.1145/3292500.3330744

Cited by 75 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SML techniques for automatic text classification are widely used in industrial environments, where commercial success is paramount (see e.g. Bernardi et al 2019). Although promising social science applications are also known, it is still an open question whether these methods can be used to solve social science problems outside the scope of hermeneutically more trivial cases (for some of the challenges, see Chen et al 2018).…”

Section: Big Textual Data In Psychological Researchmentioning

confidence: 99%

Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities

et al. 2022

View full text Add to dashboard Cite

Supervised machine learning on textual data has successful industrial/business applications, but it is an open question whether it can be utilized in social knowledge building outside the scope of hermeneutically more trivial cases. Combining sociology and data science raises several methodological and epistemological questions. In our study the discursive framing of depression is explored in online health communities. Three discursive frameworks are introduced: the bio-medical, psychological, and social framings of depression. ~80 000 posts were collected, and a sample of them was manually classified. Conventional bag-of-words models, Gradient Boosting Machine, word-embedding-based models and a state-of-the-art Transformer-based model with transfer learning, called DistilBERT were applied to expand this classification on the whole database. According to our experience ‘discursive framing’ proves to be a complex and hermeneutically difficult concept, which affects the degree of both inter-annotator agreement and predictive performance. Our finding confirms that the level of inter-annotator disagreement provides a good estimate for the objective difficulty of the classification. By identifying the most important terms, we also interpreted the classification algorithms, which is of great importance in social sciences. We are convinced that machine learning techniques can extend the horizon of qualitative text analysis. Our paper supports a smooth fit of the new techniques into the traditional toolbox of social sciences.

show abstract

Section: Big Textual Data In Psychological Researchmentioning

confidence: 99%

Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Furthermore, eight studies reported construction practices and guidelines for AI systems based on diverse experiences, such as implementing ML components to detect and correct transaction errors in SAP [160], a systematic comparison of DL frameworks and platforms [71], experiences from improving Airbnb search results with DL [73], experiences from 150 ML applications at Booking.com [21], AI model criteria relevant for end users [56], automatic version control in notebooks [98], or practices collected via practitioner surveys and/or interviews [203,224]. Similarly, seven studies reported current challenges in constructing AI-based systems, most of them focusing on DL.…”

Section: Software Construction (23 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

View full text Add to dashboard Cite

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

show abstract

“…A study by Expedia Group Media Solutions based on Millward Brown Digital clickstream data outlines this and the need for an OTA to meet many requirements across a traveler's booking journey. Bernardi et al (2019) made this point for Booking.com, listing a number of advanced algorithms they have implemented that use such data to improve their site experience.…”

Section: The Traveler Online Booking Journeymentioning

confidence: 99%