Explaining the output of ensembles in medical decision support on a case by case basis

Wall, Robert; Cunningham, Pádraig; Walsh, Paul; Byrne, Stephen J.

doi:10.1016/s0933-3657(03)00056-3

Cited by 31 publications

(10 citation statements)

References 12 publications

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“…With involvement of multiple classifiers in decision-making, it is more difficult for non-expert users to perceive the underlying reasoning process leading to a decision. A first attempt for extracting meaningful rules from ensembles was presented in (Wall et al 2003).…”

Section: Discussionmentioning

confidence: 99%

Machine learning: a review of classification and combining techniques

2006

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Supervised classification is one of the tasks most frequently carried out by socalled Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy-ensembles of classifiers.

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Section: Discussionmentioning

confidence: 99%

Machine learning: a review of classification and combining techniques

2006

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“…16 Mapping functions may also be combined in a given classification task to form an ensemble, based on the principle that a committee of predictors prone to nonidentical errors will prove more accurate than any given predictor alone. 17 Ensembles may be generated by applying the same mapping function to subsets of data, training the same function using different sets of parameters, or by combining different mapping functions, overall resulting in improved accuracy at the expense of computation time, data size, and comprehensibility. 18 Along similar lines, automated machine learning (auto-ML) approaches the selection of a mapping function and its parameters as a machine learning task, allowing for selection and optimization of a mapping function or ensemble of functions with little user input.…”

Section: Key Pointsmentioning

confidence: 99%

Machine learning applications in epilepsy

2019

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Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

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“…This directly corresponds to the usefulness of ensemble learning that is grounded on the classifier having dissimilar ensemble strategies. This improvement in forecasting performance unfortunately comes at the cost of comprehensibility as, by definition, an ensemble mechanism is considerably more complicated than its base classifiers [46]. This is important for computer-assisted systems in financial risk assessment or medicine domains, because of the reluctance to implement mechanisms that are essentially black-box classifiers.…”

Section: Performance Assessment and Comparison Of Resultsmentioning

confidence: 99%

An emerging hybrid mechanism for information disclosure forecasting

Hsu

Lin

2014

Int. J. Mach. Learn. & Cyber.

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Corporate governance mechanisms ensure that investors get a fair return on their investment. A wellestablished governance mechanism reduces the information asymmetry and agency cost between a firm's management and stakeholders, but decision makers find it difficult to assess the corporate governance status of publicly-listed firms before the annual official announcement the following year. This study proposes a hybrid ensemble learning forecasting mechanism (HELM), whose single-component candidates from the extreme learning machine (ELM) algorithm with dissimilar ensemble strategies (that is, data diversity, parameter diversity, kernel diversity, and preprocessing diversity) form one initial dataset. We implement locally linear embedding into the proposed mechanism to handle the dimensionality task and then utilize the weighted voting taken from the base components' crossvalidation performance on a training dataset as the integration mechanism. Experimental results show that the proposed HELM significantly outperforms the other classifiers, but its superior performance under many real-life application domains comes with a critical drawback: it is incapable of providing an explanation for the underlying reasoning mechanisms. Thus, this study advances the utilized rough set theory with its explanation capability to extract the inherent knowledge from the ensemble mechanism (HELM). The informative rules can be used as a guideline for decision makers to make a reliable judgment under turbulent financial markets.

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Explaining the output of ensembles in medical decision support on a case by case basis

Cited by 31 publications

References 12 publications

Machine learning: a review of classification and combining techniques

Machine learning: a review of classification and combining techniques

Machine learning applications in epilepsy

An emerging hybrid mechanism for information disclosure forecasting

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