Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction

Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette

doi:10.1016/j.ejor.2020.02.036

Cited by 11 publications

(10 citation statements)

References 24 publications

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“…However, we note that, in class imbalance data, different classes always have different number of samples, which means the relative probability densities in different classes calculated by (8) could not be compared directly, as they are scaled in different scales. To make them comparable, we should conduct an extra normalization step.…”

Section: Probability Density Machine (Pdm) Algorithmmentioning

confidence: 99%

“…Learning from imbalanced data is an important and hot topic in machine learning, as it has been widely applied to diagnose and classify diseases [1,2], detect software defects [3,4], analyze biology and pharmacology data [5,6], evaluate credit risk [7], predict actionable revenue change and bankruptcy [8,9], diagnose faults in the industrial procedure [10,11], classify soil types [12,13], and even predict crash injury severity [14] or analyze crime linkages [15]. Meanwhile, class imbalance learning (CIL) is also a challenging task.…”

Section: Introductionmentioning

confidence: 99%

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Probability Density Machine: A New Solution of Class Imbalance Learning

Cheng

Longfei

et al. 2021

Scientific Programming

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Class imbalance learning (CIL) is an important branch of machine learning as, in general, it is difficult for classification models to learn from imbalanced data; meanwhile, skewed data distribution frequently exists in various real-world applications. In this paper, we introduce a novel solution of CIL called Probability Density Machine (PDM). First, in the context of Gaussian Naive Bayes (GNB) predictive model, we analyze the reason why imbalanced data distribution makes the performance of predictive model decline in theory and draw a conclusion regarding the impact of class imbalance that is only associated with the prior probability, but does not relate to the conditional probability of training data. Then, in such context, we show the rationality of several traditional CIL techniques. Furthermore, we indicate the drawback of combining GNB with these traditional CIL techniques. Next, profiting from the idea of K-nearest neighbors probability density estimation (KNN-PDE), we propose the PDM which is an improved GNB-based CIL algorithm. Finally, we conduct experiments on lots of class imbalance data sets, and the proposed PDM algorithm shows the promising results.

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Section: Probability Density Machine (Pdm) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probability Density Machine: A New Solution of Class Imbalance Learning

Cheng

Longfei

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Similarly, Mahajan et. al [16] offered a framework for revenue change prediction which allows for the determination of actionable decisions in a cloud services sales application. Rezazadeh et.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In our work, we apply a novel class-imbalance handling methodology based on the work of Mahajan et. al [16], where an objective loss function is defined to utilize a metaoptimization technique to tune individual sample weight parameters against a particular user-defined metric such as F1score, accuracy, or the area under the curve (AUC).…”

Section: ) Model and Hyper-parameter Tuningmentioning

confidence: 99%

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Predicting Loss Risks for B2B Tendering Processes

Zahid¹,

Ong²,

Megahed³

et al. 2021

Preprint

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Sellers and executives who maintain a bidding pipeline of sales engagements with multiple clients for many opportunities significantly benefit from data-driven insight into the health of each of their bids. There are many predictive models that offer likelihood insights and win prediction modeling for these opportunities. Currently, these win prediction models are in the form of binary classification and only make a prediction for the likelihood of a win or loss. The binary formulation is unable to offer any insight as to why a particular deal might be predicted as a loss. This paper offers a multi-class classification model to predict win probability, with the three loss classes offering specific reasons as to why a loss is predicted, including no bid, customer did not pursue, and lost to competition. These classes offer an indicator of how that opportunity might be handled given the nature of the prediction. Besides offering baseline results on the multi-class classification, this paper also offers results on the model after class imbalance handling, with the results achieving a high accuracy of 85% and an average AUC score of 0.94.

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A PCA-AdaBoost model for E-commerce customer churn prediction

Jing

et al. 2022

Ann Oper Res

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Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction

Cited by 11 publications

References 24 publications

Probability Density Machine: A New Solution of Class Imbalance Learning

Probability Density Machine: A New Solution of Class Imbalance Learning

Predicting Loss Risks for B2B Tendering Processes

A PCA-AdaBoost model for E-commerce customer churn prediction

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