Multi-Stage Convex Relaxation Methods for Machine Learning

2013 ◽  
Author(s):  
Tong Zhang
Keyword(s):  
Machine Learning ◽  
Convex Relaxation ◽  
Relaxation Methods ◽  
Multi Stage

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals Classification of Platinum Nanoparticle Catalysts using Machine Learning

2020 ◽  
Author(s):  
Amanda J. Parker ◽  
George Opletal ◽  
Amanda Barnard
Keyword(s):  
Machine Learning ◽  
Hydrogen Oxidation ◽  
Pt Nanoparticles ◽  
Structure Property ◽  
Metallic Nanoparticle ◽  
Platinum Nanoparticle ◽  
Nanoparticle Catalysts ◽  
Structure Property Relationships ◽  
Multi Stage

Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab.

Abstract TP458: High Accuracy of Predictive Models for SAH Using Different Machine Learning Approaches

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Paul Litvak ◽  
Jeevan Medikonda ◽  
Girish Menon ◽  
Pitchaiah Mandava
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Prediction Accuracy ◽  
Support Vector ◽  
World Federation ◽  
Learning Approaches ◽  
Flow Models ◽  
Multi Stage ◽  
Stage 1 ◽  
Categorical Scale

Background: Patients suffering from subarachnoid hemorrhage (SAH) have poor long-term outcomes. There are predictive models for ischemic and hemorrhagic stroke. However, there is paucity of models for SAH. Machine learning concepts were applied to build multi-stage Neural Networks (NN), Support Vector Machines (SVM) and Keras/Tensor Flow models to predict SAH outcomes. Methods: A database of ~800 aneurysmal SAH patients from Kasturba Medical College was utilized. Baseline variables of World Federation of Neurosurgeons 5-point scale (WFNS 1-5), age, gender, and presence/absence of hypertension and diabetes were considered in Stage 1. Stage 2 included all Stage 1 variables along with presence/absence of radiologic signs vasospasm and ischemia. Stage 3 includes earlier 2 stages and discharge Glasgow Outcome Scale (GOS 1-5). GOS at 3 months was predicted using 2-layer NN/SVM/Keras-TensorFlow models on the five point categorical scale as well as dichotomized to dead/alive and favorable (GOS 4-5) or unfavorable (GOS 1-3). Prediction accuracy of models was compared to the recorded GOS. Results: Prediction accuracy shown as percentages (See Table) for all three stages was similar for SVM, NN and Keras/TensorFlow models. Accuracy was remarkably higher with dichotomization compared to the complete five point GOS categorical scale. Conclusions: SVM, NN, and Keras-TensorFlow based machine learning models can be used to predict SAH outcomes to a high degree of accuracy. These powerful predictive models can be used to prognosticate and select patients into trials.

scholarly journals Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking

2019 ◽  
Vol 33 ◽  
pp. 2189-2196 ◽  
Author(s):  
Jun Wang ◽  
Sujoy Sikdar ◽  
Tyler Shepherd ◽  
Zhibing Zhao ◽  
Chunheng Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Search Algorithms ◽  
Voting Rules ◽  
Practical Algorithms ◽  
Multi Stage ◽  
Parallel Universes ◽  
Np Complete

STV and ranked pairs (RP) are two well-studied voting rules for group decision-making. They proceed in multiple rounds, and are affected by how ties are broken in each round. However, the literature is surprisingly vague about how ties should be broken. We propose the first algorithms for computing the set of alternatives that are winners under some tiebreaking mechanism under STV and RP, which is also known as parallel-universes tiebreaking (PUT). Unfortunately, PUT-winners are NP-complete to compute under STV and RP, and standard search algorithms from AI do not apply. We propose multiple DFS-based algorithms along with pruning strategies, heuristics, sampling and machine learning to prioritize search direction to significantly improve the performance. We also propose novel ILP formulations for PUT-winners under STV and RP, respectively. Experiments on synthetic and realworld data show that our algorithms are overall faster than ILP.

Multi-stage all-zero block detection for HEVC coding using machine learning

2020 ◽  
Vol 73 ◽  
pp. 102945
Author(s):  
Haibing Yin ◽  
Haoyun Yang ◽  
Xiaofeng Huang ◽  
Hongkui Wang ◽  
Chenggang Yan
Keyword(s):  
Machine Learning ◽  
Multi Stage

scholarly journals A multi-stage machine learning model for diagnosis of esophageal manometry

2021 ◽  
pp. 102233
Author(s):  
Wenjun Kou ◽  
Dustin A. Carlson ◽  
Alexandra J. Baumann ◽  
Erica N. Donnan ◽  
Jacob M. Schauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Esophageal Manometry ◽  
Learning Model ◽  
Multi Stage ◽  
Machine Learning Model
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