Learning Random Forest from Histogram Data Using Split Specific Axis Rotation

Abstract

He received his B. Eng. degree in computer engineering from Tribhuvan University, Nepal in 2006 and received his master's degree from Stockholm University, Sweden in 2013. His current research areas are data mining and machine learning. Tony Lindgren Tony Lindgren was born in Hä gersten, Stockholm in 1974. He received his master degree in computer and system sciences in 1999. In 2006 he received his Ph.D. degree in computer and system sciences. He has worked both in academia and industry since 2008, he is the inventor of numerous patents and has a permanent position as lecturer at the Department of Computer and System Sciences at Stockholm University since 2012. His main interest is in the field of machine learning, artificial intelligence and constraint programming.Henrik Boström is a professor in computer sciencedata science systems at KTH Royal Institute of Technology. His expertise is within machine learning, primarily ensemble learning, rule and decision tree learning and conformal prediction. He is an action editor of the machine learning journal and on the editorial boards of Journal of Machine Learning Research, Knowledge Discovery and Data Mining, and Intelligent Data Analysis.

An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic

“…The machine learning models analyse the historical time-series data to forecast the number of confirmed COVID-19 cases in the future. For this, a sliding-window method (Ram Gurung & Bostrom, 2018 ) is used to transform the time-series dataset into supervised learning by segmenting the time-series records as daily windows. Each machine learning model is trained by the normalised results of the actual/historical number of COVID-19 confirmed cases and mobility ratio to forecast the number of future COVID-19 confirmed cases.…”

Weibull recurrent neural networks for failure prognosis using histogram data

“…These include the works of [9,32], and [12][13][14] who investigated compressor, battery failures, and NOx sensor failures in heavy-duty trucks, respectively. [9] did not clearly outline the preprocessing steps while using the histogram data, and the study of [32] was limited by the small fleet size used for analysis.…”

“…[9] did not clearly outline the preprocessing steps while using the histogram data, and the study of [32] was limited by the small fleet size used for analysis. The closest of the above three works to the case study discussed in this paper is that of [12][13][14] who used a dataset very similar to the one used here, but for a different prognosis technique and target component. The authors in [12][13][14] used random forest algorithm for classifying the data corresponding to failure and non-failure class.…”

“…The closest of the above three works to the case study discussed in this paper is that of [12][13][14] who used a dataset very similar to the one used here, but for a different prognosis technique and target component. The authors in [12][13][14] used random forest algorithm for classifying the data corresponding to failure and non-failure class. However, their technique relied on recursively combining the histogram bins using a sliding window, in contrast to the entropy of the histogram distributions used in this paper.…”