Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation

Autor:	Batarseh, Feras A., Kulkarni, Ajay
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Social and Information Networks Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the premise that context can aid in mitigating these issues. In a traditional data science lifecycle, context is not considered. Context-driven Data Science Lifecycle (C-DSL); the main contribution of this paper, is developed to address these challenges. Two case studies (using data-sets from sports events) are developed to test C-DSL. Results from both case studies are evaluated using common data mining metrics such as: coefficient of determination (R2 value) and confusion matrices. The work presented in this paper aims to re-define the lifecycle and introduce tangible improvements to its outcomes. Comment: 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019) At SIAM - Society for Industrial and Applied Mathematics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.08670 Zobrazit plný text záznamu View this record from Arxiv