LDT-MRF: Log decision tree and map reduce framework to clinical big data classification
نویسندگان
چکیده
منابع مشابه
Classification Algorithms for Big Data Analysis, a Map Reduce Approach
Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), whic...
متن کاملBig Data Processing with Hadoop Map-reduce
The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data ...
متن کاملLand Cover Classification Using IRS-1D Data and a Decision Tree Classifier
Land cover is one of basic data layers in geographic information system for physical planning and environmentalmonitoring. Digital image classification is generally performed to produce land cover maps from remote sensing data,particularly for large areas. In the present study the multispectral image from IRS LISS-III image along with ancillary datasuch as vegetation indices, principal componen...
متن کاملPredicting Twist Condition by Bayesian Classification and Decision Tree Techniques
Railway infrastructures are among the most important national assets of countries. Most of the annual budget of infrastructure managers are spent on repairing, improving and maintaining railways. The best repair method should consider all economic and technical aspects of the problem. In recent years, data analysis of maintenance records has contributed significantly for minimizing the costs. B...
متن کاملBig Data Classification Using Augmented Decision Trees
We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as random forests or gradient boosted trees. Unlike ensemble methods, the models produced by the algorithm can be easily interpreted. The algorithm is based on a divide and conquer strategy and consists of two steps. Th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Engineering & Technology
سال: 2017
ISSN: 2227-524X
DOI: 10.14419/ijet.v7i1.5.9129