Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data

Zhu, Min; Xia, Jing; Jin, Xiaoqing; Yan, Ming; Cai, Guolong; Yan, Jing; Ning, Gangmin

doi:10.1109/access.2018.2789428

Cited by 145 publications

(68 citation statements)

References 92 publications

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“…The time complexity of this step is Ο( N 2 ) . Then the kernel matrix is performed eigenvalue decomposition; the time complexity of this step is Ο( N 3 ) . Then the empirical mapping form of the samples in the kernel space is obtained by using Equation .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, this paper firstly divides the information of HF patient into diagnostic view, medication view, and examination view. Furthermore, considering the imbalance of HF data, under sampling is used to reduce the imbalance rate (IR). Compared with the original dataset, the obtained data subset not only has a much lower IR but also reduces the time complexity in empirical kernel mapping .…”

Section: Introductionmentioning

confidence: 99%

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Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction

Wang

Chen

Zhang

et al. 2019

Numer Methods Biomed Eng

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Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi-view ensemble learning algorithm based on empirical kernel mapping called MVE-EK, which predicts the mortality of patient through hospital records. Multi-view ensemble learning can take advantage of the consistency and complementarity of different views. The MVE-EK first divides the patient's features into multiple views and then divides the samples of each view to multiple subsets through under sampling, which can reduce the imbalance rate of the original dataset and obtain some relatively balanced subsets. Each subset is mapped into kernel space by empirical kernel mapping, which can map samples from linearly inseparable spaces to linearly separable spaces. Finally, the multi-view ensemble learning is performed by the designed loss of acquaintance between views. The effectiveness of the algorithm is verified on the three datasets of HF patient in the real world. The performance of the algorithm is better than other comparison algorithms. The datasets are collected from Shanghai Shuguang Hospital and involve 10 203 hospitalization records for 4682 HF patients between March 2009 and April 2016. The prediction information provided by the algorithm can assist the clinician in providing a more personalized treatment plan for patients with HF.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction

Wang

Chen

Zhang

et al. 2019

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…In order to fully demonstrate the performance of the EPNet proposed in this paper, this chapter includes comparisons between Support Vector Machine (SVM) [25][26][27][28][29][30], Random Forest (RF) [31][32][33][34][35][36], Decision Tree (DT) [37][38][39][40][41][42], MLP, CNN and LSTM. Figure 6 is the Electric Power Markets (PJM) Regulation Zone Preliminary Billing Data [43] used in this experiment, this data records the regulation market capacity clearing price of every half hour in 2017.…”

Section: Resultsmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

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“… Data sampling approaches 7–9 rebalance the data sets by sampling, which is achieved by over‐sampling the minority class, 7 under‐sampling the majority class 10 or a hybrid of both 11 Cost‐sensitive learning approaches 4,12 incorporate the costs of misclassifying minority class samples into function minimization. Algorithmic modification approaches 13,14 are the modifications of commonly used machine learning algorithms to achieve better performance with imbalanced data set.…”

Section: Related Workmentioning

confidence: 99%

A self‐adaptive synthetic over‐sampling technique for imbalanced classification

Angelov

Soares

2020

Int J Intell Syst

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Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest-for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the socalled fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In Xiaowei Gu and Plamen P. Angelov contributed equally to this study.

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Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data

Cited by 145 publications

References 92 publications

Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction

Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

A self‐adaptive synthetic over‐sampling technique for imbalanced classification

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