Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns

Jhaveri, Siddharth; Khedkar, Ishan; Kantharia, Yash; Jaswal, Shree

doi:10.1109/iccmc.2019.8819828

Cited by 66 publications

(31 citation statements)

References 6 publications

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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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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Park

Kim

et al. 2020

Preprint

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“…Each model reduces the errors of its previous model until the maximum accuracy is achieved or the number of models increases. It uses the model generated by random forest as the base estimator to generate the best results 40 . A number of excellent models have been derived from the AdaBoost formation idea, including XGBoost, LightGBM, and CatBoost, as shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

“…It uses the model generated by random forest as the base estimator to generate the best results. 40 A number of excellent models have been derived from the AdaBoost formation idea, including XGBoost, LightGBM, and CatBoost, as shown in Figure 3.…”

Section: Adaptive Boosting (Adaboost)mentioning

confidence: 99%

Study on the influence of surrounding urban SO₂, NO₂, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

Zhang

Chen

et al. 2020

Concurrency and Computation

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The formation of haze depends on the complex evolution behavior of SO 2 , NO 2 , and CO. We explore the influence of surrounding urban oxides on haze formation in Beijing. From the perspective of time evolution, multifractal detrended cross-correlation analysis (MF-DCCA) method quantitatively shows that all oxides reveal persistent cross-correlations (h xy : 0.640 sim; 0.995) in the short term and long term. The correlation characteristics of the same oxide in different regions are compared. In the short term, Zhangjiakou (SO 2 /NO 2) and Tianjin (CO) have the strongest multifractal features, while Zhangjiakou (SO 2 /NO 2 /CO) is more prominent in the long term. Their sources are mainly Fat-tailed. Ignoring the characteristics of timing, the fitting degree (R 2) of LightGBM model to PM2.5 concentration regression is 0.862 after the addition of neighboring oxides, an increase of 2.0%. CatBoost's R 2 improves by 6.4%. The feature importance score indicates that Langfang's SO 2 and Chengde's CO contribute the most to the formation of PM2.5. This study has certain reference value for the formulation of oxide emission strategy in the surrounding area of Beijing.

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“…• Random Forest uses decision trees, which are very prone to success [13]; typically, it is used to achieve higher accuracy, based on different sets of attributes and samples. Random forest is known for fast training, matching speed, high classification accuracy and good performance for high-dimensional input data [14,15].…”

Section: Introductionmentioning

confidence: 99%

Improving the performance of a radio-frequency localization system in adverse outdoor applications

Sousa

Santana

Fernandes

et al. 2021

J Wireless Com Network

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In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

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Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns

Cited by 66 publications

References 6 publications

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Study on the influence of surrounding urban SO₂, NO₂, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

Improving the performance of a radio-frequency localization system in adverse outdoor applications

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Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns

Cited by 66 publications

References 6 publications

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Study on the influence of surrounding urban SO2, NO2, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

Improving the performance of a radio-frequency localization system in adverse outdoor applications

Contact Info

Product

Resources

About

Study on the influence of surrounding urban SO₂, NO₂, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms