Review on Class Imbalance Learning: Binary and Multiclass

Singh, Ranjana; Raut, Roshani

doi:10.5120/ijca2015907573

Cited by 5 publications

(9 citation statements)

References 19 publications

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“…13, K ck = FIGURE15: The misclassification error information is embedded in the Mahanalobis distances obtained using the matrices of probabilities of the classes (majority and minority) before and after permuting X i (each class corresponds to a column in the matrix). This figure illustrates the effect on the Mahanalobis distances when permuting the values of Xi when dealing with imbalanced datasets (class distribution [10,280,10]…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Content may change prior to final publication. 90,10] and [10,280,10]. Each realization measures the error generated by the CIT before and after permuting X i .…”

Section: ) Transform the Multiclass Output Datasetmentioning

confidence: 99%

“…The class distribution for this setting [BR (5,10) Since the input variables are defined to be independent, we can evaluate the VIT based on GSA as well as the mhχ 2 algorithm against the Decision Tree-based techniques (VSURF, Boruta, VarSelRF, VUS).…”

Section: B Nonlinear Modelmentioning

confidence: 99%

“…removing a subset of majority classes, see RUS or Random Under-Sampling [9]). A comparison of sampling methods applied to the imbalanced classification problem can be found in [10]- [12]. The strategy behind the cost-sensitive learning is to adjust the machine learning technique in order to minimize the misclassification cost.…”

Section: Introductionmentioning

confidence: 99%

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Variable Importance Analysis in Imbalanced Datasets: A New Approach

et al. 2020

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Decision-making using machine learning requires a deep understanding of the model under analysis. Variable importance analysis provides the tools to assess the importance of input variables when dealing with complex interactions, making the machine learning model more interpretable and computationally more efficient. In classification problems with imbalanced datasets, this task is even more challenging. In this article, we present two variable importance techniques, a nonparametric solution, called mh-χ 2 , and a parametric method based on Global Sensitivity Analysis. The mh-χ 2 employs a multivariate continuous response framework to deal with the multiclass classification problem. Based on the permutation importance framework, the proposed mh-χ 2 algorithm captures the dissimilarities between the distribution of misclassification errors generated by the base learner, Conditional Inference Tree, before and after permuting the values of the input variable under analysis. The GSA solution is based on the Covariance decomposition methodology for multivariate output models. Both solutions will be assessed in a comparative study of several Random Forest-based techniques with emphasis in the multiclass classification problem with different imbalanced scenarios. We apply the proposed techniques in two real application cases in order first, to quantify the importance of the 35 companies listed in the Spanish market index IBEX35 on the economic, political and social uncertainties reflected in economic newspapers in Spain during the first quadrimester of 2020 due to the COVID-19 pandemic and second, to assess the impact of energy factors on the occurrence of spike prices on the Spanish electricity market.

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Section: Discussion and Limitationsmentioning

confidence: 99%

“…Content may change prior to final publication. 90,10] and [10,280,10]. Each realization measures the error generated by the CIT before and after permuting X i .…”

Section: ) Transform the Multiclass Output Datasetmentioning

confidence: 99%

Section: B Nonlinear Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variable Importance Analysis in Imbalanced Datasets: A New Approach

et al. 2020

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“…Conversely, a major issue associated with most of the real-world data sets is class imbalanced problem, which leads to reduce performance of classifiers [26][27][28]. The standard PNN algorithm does not facilitate handling the multi-class imbalanced problem in the classification of cost sensitive data which is very frequent in reality [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

2019

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Financial market prediction attracts immense interest among researchers nowadays due to rapid increase in the investments of financial markets in the last few decades. The stock market is one of the leading financial markets due to importance and interest of many stakeholders. With the development of machine learning techniques, the financial industry thrived with the enhancement of the forecasting ability. Probabilistic neural network (PNN) is a promising machine learning technique which can be used to forecast financial markets with a higher accuracy. A major limitation of PNN is the assumption of Gaussian distribution as the distribution of input variables which is violated with respect to financial data. The main objective of this study is to improve the standard PNN by incorporating a proper multivariate distribution as the joint distribution of input variables and addressing the multi-class imbalanced problem persisting in the directional prediction of the stock market. This model building process is illustrated and tested with daily close prices of three stock market indices: AORD, GSPC and ASPI and related financial market indices. Results proved that scaled t distribution with location, scale and shape parameters can be used as more suitable distribution for financial return series. Global optimization methods are more appropriate to estimate better parameters of multivariate distributions. The global optimization technique used in this study is capable of estimating parameters with considerably high dimensional multivariate distributions. The proposed PNN model, which considers multivariate scaled t distribution as the joint distribution of input variables, exhibits better performance than the standard PNN model. The ensemble technique: multi-class undersampling based bagging (MCUB) was introduced to handle class imbalanced problem in PNNs is capable enough to resolve multi-class imbalanced problem persisting in both standard and proposed PNNs. Final model proposed in the study with proposed PNN and proposed MCUB technique is competent in forecasting the direction of a given stock market index with higher accuracy, which helps stakeholders of stock markets make accurate decisions.

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Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction

Gill

Moullet

Martinsson

et al. 2022

CPT Pharmacom & Syst Pharma

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The gold‐standard approach for modeling pharmacokinetic mediated drug–drug interactions is the use of physiologically‐based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug‐specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system‐specific parameters. Machine learning has the potential to be utilized for the prediction of drug–drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine‐learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically‐based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case‐by‐case basis. Therefore, they may be appropriate for later stages of drug–drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine‐learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug–drug interaction risk assessment across the stages of drug discovery and development.

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Review on Class Imbalance Learning: Binary and Multiclass

Cited by 5 publications

References 19 publications

Variable Importance Analysis in Imbalanced Datasets: A New Approach

Variable Importance Analysis in Imbalanced Datasets: A New Approach

An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction

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