Stacking for machine learning redshifts applied to SDSS galaxies

Zitlau, Roman; Hoyle, B.; Paech, K.; Weller, J.; Rau, Markus; Seitz, S.

doi:10.1093/mnras/stw1454

Cited by 23 publications

(14 citation statements)

References 57 publications

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“…The study [34], eulogize the performance of AC with mathematical ground truth in their research. In many classification experiments, the AC has been shown to outperform the other machine learning algorithms [35]- [37]. For a detailed discussion of AC, see [38]…”

Section: ) Adaboost Classifier (Ac)mentioning

confidence: 99%

Classification of Shopify App User Reviews Using Novel Multi Text Features

et al. 2020

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App stores usually allow users to give reviews and ratings that are used by developers to resolve issues and make plans for their apps. In this way, these app stores collect large amounts of data for analysis. However, there are several challenges that must first be addressed, related to redundancy and the volume of data, by using machine learning. This study performs experiments on a dataset that contains reviews for Shopify apps. To overcome the aforementioned limitations, we first categorize user reviews into two groups, i.e., happy and unhappy, and then perform preprocessing on the reviews to clean the data. At a later stage, several feature engineering techniques, such as bag-of-words, term frequency-inverse document frequency (TF-IDF), and chi-square (Chi2), are used singly and in combination to preserve meaningful information. Finally, the random forest, AdaBoost classifier, and logistic regression models are used to classify the reviews as happy or unhappy. The performance of our proposed pipeline was evaluated using average accuracy, precision, recall, and f 1 score. The experiments reveal that a combination of features can improve machine learning models performance and in this study, logistic regression outperforms the others and achieves an 83% true acceptance rate when combined with TF-IDF and Chi2.

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Section: ) Adaboost Classifier (Ac)mentioning

confidence: 99%

Classification of Shopify App User Reviews Using Novel Multi Text Features

et al. 2020

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“…18,19 Several studies have reported higher performance with Adaboost compared to XGBoost, despite the popularity of XGBoost. 20,21,22,23,24,25,26 Compared with the previous studies, this study proposes the following contributions. First, it is a novel approach to apply and compare the extensive range of data-driven machine learning algorithms for nomograms subject to SMILE.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Park

Kim

et al. 2020

Preprint

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Background: Small-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm. Methods: We retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root mean square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated.Results: Adaboost achieved the highest performance with an RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was pre-operative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon. Conclusions: Among the diverse machine learning algorithms, Adaboost exhibited the highest performance in prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.

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“…The first two classes of methods work with a single algorithm called "weak" to generate a stronger model. While combinatorial methods combine several algorithms at once in order to have a more powerful predictive model (Nagi & Bhattacharyya, 2013) (Zitlau et al, 2016) (Alves, 2017).…”

Section: Machine Learnig Algorithmsmentioning

confidence: 99%

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Mourdi¹,

Sadgal²,

Fathi³

et al. 2020

Turkish Online Journal of Distance Education

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At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC. Proposed more and more by universities and training centers around the world, MOOCs have become an undeniable asset for any student or person seeking to complete their initial training with free distance courses open to all areas. Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%. This rate significantly affects the efforts made by the moderators for the success of this pedagogical model and negatively influences the learners' experience and their supervision. To address this problem and help instructors streamline their interventions, we present a solution to classify MOOC learners into three distinct classes. The approach proposed in this paper is based on the filters methods to select the most relevant attributes and ensembling methods of machine learning algorithms. This approach has been validated by four MOOC courses from Stanford University. In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.

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Stacking for machine learning redshifts applied to SDSS galaxies

Cited by 23 publications

References 57 publications

Classification of Shopify App User Reviews Using Novel Multi Text Features

Classification of Shopify App User Reviews Using Novel Multi Text Features

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

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