Proposal of Effective Preprocessing Techniques of Financial Data

Abasova, Jela; Jánošík, Ján; Simoncicova, Veronika; Tanuška, Pavol

doi:10.1109/ines.2018.8523922

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To our knowledge, there is no formal standard for methodology in the data science projects (see Saltz and Shamshurin, 2016 ). Through the years, the CRISP-DM methodology (Shearer, 2000 ) created in the late 1990s has become a de-facto standard, as evidenced from a range of works (see, e.g., Huang et al, 2014 ; Niño et al, 2015 ; Fahmy et al, 2017 ; Pradeep and Kallimani, 2017 ; Abasova et al, 2018 ; Ahmed et al, 2018 ). An important factor of its success is the fact that it is industry, tool, and application agnostic (Mariscal et al, 2010 ).…”

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Kolyshkina¹,

Simoff

2021

Front. Big Data

View full text Add to dashboard Cite

Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process.

show abstract