Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest

Chen, Jiaying; Liu, Xiao-cong; Wei, Hang; Chen, Qinqun; Hong, Jiaming; Li, Qiong-na; Zhang, Hao

doi:10.1007/978-3-030-34482-5_7

Cited by 10 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 2017 ). However, many methods for learning unbalanced data with these ensembles have been developed (Chen and Breiman 2004 ). We modified the models to reduce the effect of skewed data, and generally improve the prediction results.…”

Section: Models and Resultsmentioning

confidence: 99%

Dank or not? Analyzing and predicting the popularity of memes on Reddit

et al. 2021

View full text Add to dashboard Cite

Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other.

show abstract

Section: Models and Resultsmentioning

confidence: 99%

Dank or not? Analyzing and predicting the popularity of memes on Reddit

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To handle the imbalanced data, the “class_weight” argument of the random forest algorithm was set to “balanced”, which penalises misclassification of the minority class (i.e. the positive samples) 21 . The remaining parameters of the random forest model were left to the default settings of the scikit-learn Python library (please refer to the “ Random forest settings ” section in the Methods) 22 .…”

Section: Resultsmentioning

confidence: 99%

Random forest classification for predicting lifespan-extending chemical compounds

Kapsiani

Howlin

2021

Sci Rep

View full text Add to dashboard Cite

Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

show abstract

“…With this experiment, we want to compare our proposed approach for learning from highly imbalanced data with methods that are already established in this field. For comparison, we used three methods: Balanced Random Forest (BRF) [ 74 ], EasyEnsamble (EE) [ 75 ], and Balanced Bagging (BB) [ 76 ]. BRF trains a classifier in which each tree of the forest will be provided balanced bootstrap samples.…”

Section: Resultsmentioning

confidence: 99%

Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels

Stankoski

Jordan

Gjoreski

et al. 2021

Sensors

View full text Add to dashboard Cite

Understanding people’s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist.

show abstract

Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest

Cited by 10 publications

References 17 publications

Dank or not? Analyzing and predicting the popularity of memes on Reddit

Dank or not? Analyzing and predicting the popularity of memes on Reddit

Random forest classification for predicting lifespan-extending chemical compounds

Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels

Contact Info

Product

Resources

About