Permeability Prediction from Specific Area, Porosity and Water Saturation using Extreme Learning Machine and Decision Tree Techniques: A Case Study from Carbonate Reservoir

2013 ◽  
Author(s):  
M. Sitouah ◽  
M. Salmeen ◽  
S. Oyemakinde ◽  
F. Anifowose ◽  
O. Abdullatif
Keyword(s):  
Decision Tree ◽  
Extreme Learning Machine ◽  
Water Saturation ◽  
Specific Area ◽  
Carbonate Reservoir ◽  
Permeability Prediction ◽  
Learning Machine

scholarly journals MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Chen ◽  
Jun Feng ◽  
Xia Sun ◽  
Nannan Wu ◽  
Zhengzheng Yang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Extreme Learning Machine ◽  
Hybrid Algorithm ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Wide Range ◽  
Good Classification ◽  
High Dropout Rate ◽  
Learning Machine

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

Extreme Learning Machine for Credit Risk Analysis

2018 ◽  
Vol 29 (1) ◽  
pp. 640-652
Author(s):  
Mais Haj Qasem ◽  
Loai Nemer
Keyword(s):  
Risk Analysis ◽  
Decision Tree ◽  
Credit Risk ◽  
Extreme Learning Machine ◽  
Financial Institutions ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Statistical Measures ◽  
Credit Risk Analysis ◽  
Learning Machine

Abstract Credit risk analysis is important for financial institutions that provide loans to businesses and individuals. Banks and other financial institutions generally face risks that are mostly of financial nature; hence, such institutions must balance risks and returns. Analyzing or determining risk levels involved in credits, finances, and loans can be performed through predictive analytic techniques, such as an extreme learning machine (ELM). In this work, we empirically evaluated the performance of an ELM for credit risk problems and compared it to naive Bayes, decision tree, and multi-layer perceptron (MLP). The comparison was conducted on the basis of a German credit risk dataset. The simulation results of statistical measures of performance corroborated that the ELM outperforms naive Bayes, decision tree, and MLP classifiers by 1.8248%, 16.6346%, and 5.8934%, respectively.

Sign in / Sign up

Export Citation Format

Share Document