Homogeneous Ensemble Methods for the Prediction of Number of Faults

2019 ◽  
pp. 31-45
Author(s):  
Santosh Singh Rathore ◽  
Sandeep Kumar
Keyword(s):  
Ensemble Methods ◽  
Homogeneous Ensemble

IMPROVING MATRIX FACTORIZATION-BASED RECOMMENDER VIA ENSEMBLE METHODS

2011 ◽  
Vol 10 (03) ◽  
pp. 539-561 ◽  
Author(s):  
XIN LUO ◽  
YUANXIN OUYANG ◽  
XIONG ZHANG
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Training Sequences ◽  
Forward Stepwise ◽  
Additive Regression ◽  
Training Examples ◽  
Homogeneous Ensemble

One of the most popular approaches to Collaborative Filtering is based on Matrix Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy by homogeneous ensemble methods. To build such ensembles, we investigate a series of methods primarily in two aspects: (i) manipulating the training examples, including bagging, AdaBoost, and Forward Stepwise Additive Regression; (ii) injecting randomness to the base models' training settings, including randomizing the initializing parameters and randomizing the training sequences. Each method is evaluated on two large, real datasets, and then the effective methods are combined to form a cascade MF ensemble scheme. The validation results on experiment datasets demonstrate that compared to a single MF-based recommender, our ensemble scheme could obtain a significant improvement in the prediction accuracy.

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

2020 ◽  
pp. 615-631
Author(s):  
Abdullateef O. Balogun ◽  
Fatimah B. Lafenwa-Balogun ◽  
Hammed A. Mojeed ◽  
Victor E. Adeyemo ◽  
Oluwatobi N. Akande ◽  
...  
Keyword(s):  
Ensemble Methods ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Homogeneous Ensemble

scholarly journals Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction

2020 ◽  
Vol 8 (4) ◽  
pp. 297-303
Author(s):  
Tamunopriye Ene Dagogo-George ◽  
Hammed Adeleye Mojeed ◽  
Abdulateef Oluwagbemiga Balogun ◽  
Modinat Abolore Mabayoje ◽  
Shakirat Aderonke Salihu
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diabetic Retinopathy ◽  
Ensemble Methods ◽  
Predictive Performance ◽  
Ensemble Classification ◽  
Support Vector ◽  
Learning Approaches ◽  
Vector Machines ◽  
Homogeneous Ensemble

Diabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base classifier to optimize predictive performance. Boosting and Bagging ensemble methods with feature selection were employed, and experiments were carried out using Python Scikit Learn libraries on DR datasets extracted from UCI Machine Learning repository. Experimental results showed that Bagged and Boosted DT were better than SVM. Specifically, Bagged DT performed best with accuracy 65.38 %, f-score 0.664, and AUC 0.731, followed by Boosted DT with accuracy 65.42 %, f-score 0.655, and AUC 0.724 when compared to SVM (accuracy 65.16 %, f-score 0.652, and AUC 0.721). These results indicate that DT's predictive performance can be optimized by employing the homogeneous ensemble methods to outperform SVM in predicting DR.

scholarly journals Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

2021 ◽  
Vol 13 (2) ◽  
pp. 238
Author(s):  
Zhice Fang ◽  
Yi Wang ◽  
Gonghao Duan ◽  
Ling Peng
Keyword(s):  
Decision Trees ◽  
Landslide Susceptibility ◽  
Ensemble Methods ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges Reservoir Area ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Rotation Forest ◽  
Predictive Values ◽  
Ensemble Technique

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

Prioritization of water erosion–prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran

2021 ◽  
Author(s):  
Hamid Reza Pourghasemi ◽  
Fatemeh Honarmandnejad ◽  
Mahrooz Rezaei ◽  
Mohammad Hassan Tarazkar ◽  
Nitheshnirmal Sadhasivam
Keyword(s):  
Ensemble Methods ◽  
Water Erosion ◽  
Southern Iran

scholarly journals Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
Author(s):  
Jianjia Wang ◽  
Xichen Wu ◽  
Mingrui Li ◽  
Hui Wu ◽  
Edwin Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ensemble Methods ◽  
Brain Regions ◽  
Fmri Data ◽  
Thermal Systems ◽  
Network Construction ◽  
Network Characteristics ◽  
Network Entropy ◽  
Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

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