Book Chapter10.1007/978-3-030-22750-0_28
Predictive Analytics with Factor Variance Association
Raul V. Ramirez-Velarde,Laura Hervert-Escobar,Neil Hernandez-Gress +2 more
- 12 Jun 2019
- pp 346-359
4
TL;DR: A Predictive Factor Variance Association (PFVA) is proposed to solve a multi-class classification problem and is robust to execute different processes simultaneously without fail as well as the accuracy of the results.
read more
Abstract: Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Nowadays, the processes are saturated in data, which must be used properly to generate the necessary key information in the decision making process. Although there are several useful techniques to process and analyze data, the main value starts with the treatment of key factors. In this way, a Predictive Factor Variance Association (PFVA) is proposed to solve a multi-class classification problem. The methodology combines well-known machine learning techniques along with linear algebra and statistical models to provide the probability that a particular sample belongs to a class or not. It can also give predictions based on regression for quantitative dependent variables and carry-out clustering of samples. The main contribution of this research is its robustness to execute different processes simultaneously without fail as well as the accuracy of the results.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
2-Hydroxypropyl-β-cyclodextrin-enhanced pharmacokinetics of cabotegravir from a nanofluidic implant for HIV pre-exposure prophylaxis.
Fernanda P. Pons-Faudoa,Fernanda P. Pons-Faudoa,Antons Sizovs,Nicola Di Trani,Nicola Di Trani,Jesus Paez-Mayorga,Jesus Paez-Mayorga,Giacomo Bruno,Giacomo Bruno,Jessica Rhudy,Madhuri Manohar,Kevin Gwenden,Cecilia Martini,Corrine Ying Xuan Chua,Greta Varchi,Mark A. Marzinke,Alessandro Grattoni +16 more
TL;DR: Overall, the data shows the potential of sustained release of βCAB via a nanofluidic implant for long-term PrEP delivery, warranting further investigation for efficacy against HIV infections.
53
Machine learning based analytical approach for geographical analysis and prediction of Boston City crime using geospatial dataset
TL;DR: The result presented in this study shows that random forest with Principle Component Analysis (PCA) improve the classification result by 9% in accuracy with comparison to simple decision tree, and PCA with decision tree gives 5% more accuracy than decision tree.
14
Creating Models for Predictive Maintenance of Field Equipment in the Oil Industry Using Simulation Based Uncertainty Modelling
TL;DR: In this paper , the authors demonstrate the efficacy of machine learning to determine time between failure, repair time (equipment downtime) and repair cost, and a mean value analysis is carried out to determine the maintenance department capacity.
Forecasting Air Pollution Contingencies Using Predictive Analytic Techniques
Raúl Ramírez-Velarde,Oscar A. Esquivel-Flores,Gerardo M. Mejia-Velazquez +2 more
TL;DR: This study develops a predictive model, PFA, to forecast severe air pollution contingencies in metropolitan areas using weather measurements, achieving high accuracy (R2: 0.7-0.8, classification accuracy: 0.74-0.98) in Monterrey's metropolitan area.
References
Principal Component Analysis
Ian T. Jolliffe
- 15 Oct 2005
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
15.1K
LIII. On lines and planes of closest fit to systems of points in space
TL;DR: This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves.
•Book
Principal Component Neural Networks: Theory and Applications
Konstantinos I. Diamantaras,Sun-Yuan Kung +1 more
- 01 Jan 1996
TL;DR: A review of Linear Algebra, Principal Component Analysis, and VLSI Implementation.
939