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ensemble method

Know About Ensemble Methods in Machine Learning - Analytics Vidhya


This article was published as a part of the Data Science Blogathon. The variance is the difference between the model and the ground truth value, whereas the error is the outcome of sensitivity to tiny perturbations in the training set. Excessive bias might cause an algorithm to miss unique relationships between the intended outputs and the features (underfitting). There is a high variance in the algorithm that models random noise in the training data (overfitting). The bias-variance tradeoff is a characteristic of a model that states to lower the bias in estimated parameters, the variance of the parameter estimated across samples has increased.

What are the Ensemble methods?


The term'ensemble methods' is used for a range of different techniques that are used to improve the accuracy of machine learning models. The overall goal of ensemble methods is to combine the predictions from multiple models in order to produce a more accurate prediction than any individual model.


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Ensemble classification methods have been shown to produce more accurate predictions than the base component models. Due to their effectiveness, ensemble approaches have been applied in a wide range of domains to improve classification. The expected prediction error of classification models can be decomposed into bias and variance. Ensemble methods that independently construct component models (e.g., bagging) can improve performance by reducing the error due to variance, while methods that dependently construct component models (e.g., boosting) can improve performance by reducing the error due to bias and variance. Although ensemble methods were initially developed for classification of independent and identically distributed (i.i.d.) data, they can be directly applied for relational data by using a relational classifier as the base component model. This straightforward approach can improve classification for network data, but suffers from a number of limitations. First, relational data characteristics will only be exploited by the base relational classifier, and not by the ensemble algorithm itself. We note that explicitly accounting for the structured nature of relational data by the ensemble mechanism can significantly improve ensemble classification. Second, ensemble learning methods that assume i.i.d.

Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook Artificial Intelligence

Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.

Building on Huang et al. GlossBERT for Word Sense Disambiguation Artificial Intelligence

We propose to take on the problem ofWord Sense Disambiguation (WSD). In language, words of the same form can take different meanings depending on context. While humans easily infer the meaning or gloss of such words by their context, machines stumble on this task.As such, we intend to replicated and expand upon the results of Huang et al.GlossBERT, a model which they design to disambiguate these words (Huang et al.,2019). Specifically, we propose the following augmentations: data-set tweaking(alpha hyper-parameter), ensemble methods, and replacement of BERT with BART andALBERT. The following GitHub repository contains all code used in this report, which extends on the code made available by Huang et al.

Ensemble Machine Learning in Python: Random Forest, AdaBoost


In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Introduction to Boosted Trees


Welcome to my new article series: Boosting algorithms in machine learning! This is Part 1 of the series. Here, I'll give you a short introduction to boosting, its objective, some key definitions and a list of boosting algorithms that we intend to cover in the next posts. You should be familiar with elementary tree-based machine learning models such as decision trees and random forests. In addition to that, it is recommended to have good knowledge of Python and its Scikit-learn library.

Boost Neural Networks by Checkpoints Artificial Intelligence

Training multiple deep neural networks (DNNs) and averaging their outputs is a simple way to improve the predictive performance. Nevertheless, the multiplied training cost prevents this ensemble method to be practical and efficient. Several recent works attempt to save and ensemble the checkpoints of DNNs, which only requires the same computational cost as training a single network. However, these methods suffer from either marginal accuracy improvements due to the low diversity of checkpoints or high risk of divergence due to the cyclical learning rates they adopted. In this paper, we propose a novel method to ensemble the checkpoints, where a boosting scheme is utilized to accelerate model convergence and maximize the checkpoint diversity. We theoretically prove that it converges by reducing exponential loss. The empirical evaluation also indicates our proposed ensemble outperforms single model and existing ensembles in terms of accuracy and efficiency. With the same training budget, our method achieves 4.16% lower error on Cifar-100 and 6.96% on Tiny-ImageNet with ResNet-110 architecture. Moreover, the adaptive sample weights in our method make it an effective solution to address the imbalanced class distribution. In the experiments, it yields up to 5.02% higher accuracy over single EfficientNet-B0 on the imbalanced datasets.

Noise-Resilient Ensemble Learning using Evidence Accumulation Clustering Artificial Intelligence

Ensemble Learning methods combine multiple algorithms performing the same task to build a group with superior quality. These systems are well adapted to the distributed setup, where each peer or machine of the network hosts one algorithm and communicate its results to its peers. Ensemble learning methods are naturally resilient to the absence of several peers thanks to the ensemble redundancy. However, the network can be corrupted, altering the prediction accuracy of a peer, which has a deleterious effect on the ensemble quality. In this paper, we propose a noise-resilient ensemble classification method, which helps to improve accuracy and correct random errors. The approach is inspired by Evidence Accumulation Clustering , adapted to classification ensembles. We compared it to the naive voter model over four multi-class datasets. Our model showed a greater resilience, allowing us to recover prediction under a very high noise level. In addition as the method is based on the evidence accumulation clustering, our method is highly flexible as it can combines classifiers with different label definitions.

A Systematic Review on the Detection of Fake News Articles Artificial Intelligence

It has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. These leverage a number of datasets, feature extraction/selection techniques and machine learning (ML) algorithms to detect fake news before it spreads. While these methods are well-documented, there is less evidence regarding their efficacy in this domain. By systematically reviewing the literature, this paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated. The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective. Finally, it is proposed that future research should focus on developing approaches that address generalisability issues (which, in part, arise from limitations with current datasets), explainability and bias.