Decision tree induction based on minority entropy for the class imbalance problem

Boonchuay, Kesinee; Sinapiromsaran, Krung; Lursinsap, Chidchanok

doi:10.1007/s10044-016-0533-3

Cited by 41 publications

(28 citation statements)

References 15 publications

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“…For , , i =2, 3, 4372, 4373, all nearest neighbors whose size up to 13 belong to one class and the neighborhood of different class appears when the nearest neighbor size becomes 15. This results in 0 for k=1, 3,5,7,9,11,13 and 0. For , , i =4, 5, …, 9, 4366, 4367, …, 4371, all nearest neighbors whose size up to 11 belong to one class and the neighbors from different class are shown when the nearest neighbor size is over 11.…”

Section: Graphical Analysis Of Nearest Neighbors Entropymentioning

confidence: 99%

“…For , , i =4, 5, …, 9, 4366, 4367, …, 4371, all nearest neighbors whose size up to 11 belong to one class and the neighbors from different class are shown when the nearest neighbor size is over 11. This results in 0 for k=1, 3,5,7,9,11, and 0 for k=13, 15. We classify , based on its number of nonzero entropies, , in Figure 3.…”

Section: Graphical Analysis Of Nearest Neighbors Entropymentioning

confidence: 99%

“…From another perspective, entropy is known to have a descriptive power of data and to quantify the degree of information's certainty [8]. Recently, Boonchuay et al [9] developed minority entropy which uses information from the minority class with decision tree induction to handle the class imbalance problem. In the field of data mining, studies combining entropy and nearest neighbors have been carried out.…”

Section: Introductionmentioning

confidence: 99%

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Instance-based entropy fuzzy support vector machine for imbalanced data

Cho

Lee

Chang

2019

Pattern Anal Applic

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Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample's neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.

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Section: Graphical Analysis Of Nearest Neighbors Entropymentioning

confidence: 99%

Section: Graphical Analysis Of Nearest Neighbors Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Instance-based entropy fuzzy support vector machine for imbalanced data

Cho

Lee

Chang

2019

Pattern Anal Applic

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“…The core measures of entropy minimization based discretization include information entropy and gain [24,25]. Let k classes be 12 , ,..., k C C C in samples set S and let ( , ) i P C S be the proportion of samples in S that has class i C .…”

Section: Data Discretizationmentioning

confidence: 99%

“…The core measures of entropy minimization based discretization include information entropy and gain [24,25]. Let k classes be C 1 , C 2 , .…”

Section: Data Discretizationmentioning

confidence: 99%

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Kim

Jin

et al. 2018

Applied Sciences

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Featured Application: The research was motivated by the requirement of a printed circuit board (PCB) manufacturer and the application of the work is to identify the repeated PCB orders for batch production according to the predicted reorder level.Abstract: Identifying the printed circuit board (PCB) orders with high reorder frequency for batch production can facilitate production capacity balance and reduce cost. In this paper, the repeated orders identification problem is transformed to a reorder level prediction problem. A prediction model based on a modified Bayesian network (BN) with Monte Carlo simulations is presented to identify related variables and evaluate their effects on the reorder level. From the historically accumulated data, different characteristic variables are extracted and specified for the model. Normalization and principal component analysis (PCA) are employed to reduce differences and the redundancy of the datasets, respectively. Entropy minimization based binning is presented to discretize model variables and, therefore, reduce input type and capture better prediction performance. Subsequently, conditional mutual information and link strength percentage are combined for the establishment of BN structure to avoid the defect of tree augmented naïve BN that easily misses strong links between nodes and generates redundant weak links. Monte Carlo simulation is conducted to weaken the influence of uncertainty factors. The model's performance is compared to three advanced approaches by using the data from a PCB manufacturer and results demonstrate that the proposed method has high prediction accuracy.

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Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees

Mulyar

Krawczyk

2018

Discovery Science

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Decision tree induction based on minority entropy for the class imbalance problem

Cited by 41 publications

References 15 publications

Instance-based entropy fuzzy support vector machine for imbalanced data

Instance-based entropy fuzzy support vector machine for imbalanced data

A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board

Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees

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