Title :
FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage
Author :
Durham, Erin-Elizabeth A. ; Xiaxia Yu ; Harrison, Robert W.
Author_Institution :
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA
Abstract :
Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today´s Data Analytics world.
Keywords :
database management systems; decision trees; fuzzy set theory; software tools; storage management; FDT 2.0; FID3 algorithm; HIV; MySQL; data analytics world; data engineering; data retention; data science; database storage; database technology; freeware FDT tool; freeware fuzzy decision tree induction tool; fuzzy decision algorithms; large biological datasets; large data handling; machine learning; microRNA; optimal user satisfaction; optimized fuzzy ID3; robust decisioning tool; sRNA; scalable supervised-classification software tool; Accuracy; Algorithm design and analysis; Databases; Decision trees; Educational institutions; Training; Training data; Big Data; HIV protease; drug resistance prediction; fuzzy ID3; fuzzy logic;
Conference_Titel :
Computational Intelligence in Healthcare and e-health (CICARE), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CICARE.2014.7007853