Advanced Interpretation of Wireline Data with Machine Learning Using Multiple Models, Augmented Learning Population and Geology

Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

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Ensemble Data Mining Methods

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch024 ◽

2008 ◽

pp. 356-363 ◽

Cited By ~ 4

Author(s):

Nikunj C. Oza

Keyword(s):

Machine Learning ◽

Data Mining ◽

Prediction Accuracy ◽

Ensemble Methods ◽

Multiple Models ◽

Machine Learning Methods ◽

Ensemble Data ◽

Basic Goal ◽

Mining Methods ◽

The Individual

Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

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The Power of Noise and the Art of Prediction

10.31235/osf.io/zu64w ◽

2017 ◽

Cited By ~ 1

Author(s):

ZhiMin Xiao ◽

Steve Higgins

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Multiple Models ◽

Generation Process ◽

Data Generation ◽

Single Model ◽

Intervention Effect ◽

Specific Data ◽

Learning Techniques

Data analysis usually aims to identify a particular signal, such as an intervention effect. Conventional analyses often assume a specific data generation process, which suggests a theoretical model that best fits the data. Machine learning techniques do not make such an assumption. In fact, they encourage multiple models to compete on the same data. Applying logistic regression and machine learning algorithms to real and simulated datasets with different features of noise and signal, we demonstrate that no single model dominates others under all circumstances. By showing when different models shine or struggle, we argue it is both possible and important to conduct comparative analyses.

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Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy

10.1101/420372 ◽

2018 ◽

Author(s):

W. Xi ◽

M.A. Beer

Keyword(s):

Machine Learning ◽

State Parameter ◽

High Accuracy ◽

Multiple Models ◽

Test Set ◽

Additional Information ◽

Feature Sharing ◽

Interaction Problem ◽

Promoter Interaction

AbstractWe report an overfitting issue in recent machine learning formulations of the enhancer-promoter interaction problem arising from the fact that many enhancer-promoter pairs share features. Cross- fold validation schemes which do not correctly separate these feature sharing enhancer-promoter pairs into one test set report high accuracy, which is actually due to overfitting. Cross-fold validation schemes which properly segregate pairs with shared features show markedly reduced ability to predict enhancer-promoter interactions from epigenomic state. Parameter scans with multiple models indicate that local epigenomic features of individual pairs of enhancers and promoters cannot distinguish those pairs that interact from those which do with high accuracy, suggesting that additional information is required to predict enhancer-promoter interactions.

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A novel approach to ensemble MLP and random forest for network security

ITM Web of Conferences ◽

10.1051/itmconf/20203203003 ◽

2020 ◽

Vol 32 ◽

pp. 03003

Author(s):

Bhushan Deore ◽

Aditya Kyatham ◽

Shubham Narkhede

Keyword(s):

Machine Learning ◽

Random Forest ◽

Network Security ◽

Multiple Models ◽

Testing Time ◽

Learning Models ◽

The Third ◽

Novel Approach ◽

Management Concept ◽

Machine Learning Models

The following paper provides a novel approach for Network Intrusion Detection System using Machine Learning and Deep Learning. This approach uses two MLP (Multi-Layer Perceptron) models one having 3 layers and other having 6 layers. Random Forest is also used for classification. These models are ensembled in such a way that the final accuracy is boosted and also the testing time is reduced. Researchers have implemented various ways for the ensemble of multiple models but we are using contradiction management concept to ensemble machine learning models. Contradiction Management concept means if two machine learning models are contradicting in their decisions (in our case 3-layer MLP and Random Forest), then the third model’s (6-layer MLP) decision is considered whose accuracy is higher than the previous models. The third model is only used for testing when the previous two models contradict in their decision because the testing time of third model is higher than the two previous models as the third model has complex architecture. This approach increased the final accuracy as ensemble of multiple models is done and also testing time has reduced. The novelty of this paper is the choice and the combination of the models for the purpose of Network security.

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The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning

10.31234/osf.io/4vq8f ◽

2021 ◽

Author(s):

Marco Del Giudice

Keyword(s):

Machine Learning ◽

Predictive Models ◽

Complex Structure ◽

Multiple Models ◽

Similar Data ◽

Scientific Applications ◽

Learning Methods ◽

Machine Learning Methods ◽

Mitigating Factors ◽

Applications Of Machine Learning

In this paper, I highlight a problem that has become ubiquitous in scientific applications of machine learning methods, and can lead to seriously distorted inferences about the phenomena under study. I call it the prediction-explanation fallacy. The fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the tradeoffs between explanation and prediction. This is a problem for at least two reasons. First, prediction-optimized models are often deliberately biased and unrealistic in order to prevent overfitting, and hence fail to accurately explain the phenomenon of interest. In other cases, they have an exceedingly complex structure that is hard or impossible to interpret, which greatly limits their explanatory value. Second, different predictive models trained on the same or similar data can be biased in different ways, so that multiple models may predict equally well but suggest conflicting explanations of the underlying phenomenon. In this note I introduce the tradeoffs between prediction and explanation in a non-technical fashion, present some illustrative examples from neuroscience, and end by discussing some mitigating factors and methods that can be used to limit or circumvent the problem.

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Ensemble Data Mining Methods

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch119 ◽

2011 ◽

pp. 770-776

Author(s):

Nikunj C. Oza

Keyword(s):

Machine Learning ◽

Data Mining ◽

Prediction Accuracy ◽

Ensemble Methods ◽

Multiple Models ◽

Machine Learning Methods ◽

Ensemble Data ◽

Basic Goal ◽

Mining Methods ◽

The Individual

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary—any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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