Comprehensive Review On Supervised Machine Learning Algorithms

Choudhary, Rishabh; Gianey, Hemant Kumar

doi:10.1109/mlds.2017.11

Cited by 141 publications

(75 citation statements)

References 12 publications

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“…Each model has unique affordances that could provide important information to an admissions officer, whether they want to know the highest possible prediction accuracy or the words most associated with a label. We used zero-rule learning [9] to establish a baseline accuracy of 58% for gender and 50% for median income.…”

Section: Methodsmentioning

confidence: 99%

AI and Holistic Review

Alvero

Arthurs

Antonio

et al. 2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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College admissions in the United States is carried out by a humancentered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant. The legitimacy and fairness of holistic review, which gives human readers significant discretion over determining each applicant's fitness for admission, has been repeatedly challenged in courtrooms and the public sphere. Using a unique corpus of 283,676 application essays submitted to a large, selective, state university system between 2015 and 2016, we assess the extent to which applicant demographic characteristics can be inferred from application essays. We find a relatively interpretable classifier (logistic regression) was able to predict gender and household income with high levels of accuracy. Findings suggest that data auditing might be useful in informing holistic review, and perhaps other evaluative systems, by checking potential bias in human or computational readings. CCS CONCEPTS • Computing methodologies → Classification and regression trees; • Applied computing → Education; Sociology.

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Section: Methodsmentioning

confidence: 99%

AI and Holistic Review

Alvero

Arthurs

Antonio

et al. 2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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“…Yet, there is evidently a broad spectrum of conceptually and in particular technically different methods that are currently being used in medical applications including imaging neuroscience. For a more detailed overview, we would like to point the reader to other, more specialized publications (Bishop, 2006;Bzdok & Ioannidis, 2019;Choudhary & Gianey, 2017;James, Witten, Hastie, & Tibshirani, 2013;Jordan & Mitchell, 2015). What is critical to note in the current context, though, is that there is generally an inverse relation between the potential accuracy or performance of machine-learning algorithms on one hand and their interpretability on the other.…”

Section: State Of the Art In Medical Aimentioning

confidence: 99%

Your evidence? Machine learning algorithms for medical diagnosis and prediction

Heinrichs

Eickhoff

2019

Human Brain Mapping

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Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues seems to be that understanding and responsibility are concepts that are intrinsically tied to the discursive practice of giving and asking for reasons. The challenge is to find ways to make the outcomes of machine learning algorithms compatible with our discursive practice. This comes down to the claim that we should try to integrate discursive elements into machine learning algorithms. Under the title of “explainable AI” initiatives heading in this direction are already under way. Extensive research in this field is needed for finding adequate solutions.

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“…There are numerous methods to find and test such models, from simple linear regression and curve-fitting to advanced machine learning methods such as decision trees, support vector machines, random forests, boosted trees, or neural networks. 59 Ensembling is the combination of models that get trained on random samples of data from the training set called bags and then combined as a whole using a voting system. This is the basis for algorithms such as Random Forests, AdaBoost, and Gradient Boosting.…”

Section: Radiomics: From Feature Extraction To Correlation With Outcomesmentioning

confidence: 99%

Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

225

130

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Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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Comprehensive Review On Supervised Machine Learning Algorithms

Cited by 141 publications

References 12 publications

AI and Holistic Review

AI and Holistic Review

Your evidence? Machine learning algorithms for medical diagnosis and prediction

Radiomics: from qualitative to quantitative imaging

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