A novel ensemble method for classifying imbalanced data

Sun, Zhongbin; Song, Qinbao; Zhu, Xiaoyan; Sun, Heli; Xu, Bin; Zhou, Yuming

doi:10.1016/j.patcog.2014.11.014

Cited by 359 publications

(143 citation statements)

References 68 publications

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“…Heli Sun et al [24] introduceda novel ensemble method, which initially changes over an imbalanced data set into multiple balanced ones and after that manufactures various classifiers on thesemultiple data with a particular classification algorithm. At last, the classification results of these classifiers for new dataare combined by a particular ensemble rule.It is a scientific approach.…”

Section: Related Workmentioning

confidence: 99%

Efficient DANNLO Classifier for Multi-class Imbalanced Data on Hadoop

2017

IJMTER

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Abstract--In recent years, multi-class imbalance data classification is a major problem in big data. In such situations, we focused on developing a new Deep Artificial Neural Network Learning Optimization (DANNLO) Classifier for large collection of imbalanced data.In our proposed work, first the dataset reduction using principal component analysis for dimensionality reduction and initial centroid is computed.Then, parallel hierarchical Pillar k-means clustering algorithm based on MapReduceis usedto partitioning of an imbalanced data set into similar subset, which can improve the computational cost. The resultant clusters are given as input to the deep ANN for learning. In the next stage, deep neural network has been trained using the back propagation algorithm. In order to optimize the n-dimensional weight space, firefly optimization algorithm is used. Attractiveness and distance of each firefly is computed. Hadoop is used to handle these large volumes of variable size data. Imbalanced datasets is taken from ECDC (European Centre for Disease Prevention and Control) repository.The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data based on TP rate, F-measure, G-mean measures, confusion matrix, precision, recall, and ROC. The experimental results suggests that DANNLO classifier exceed other ordinary classifiers such as SVM and Random forest classifier on tested imbalanced data sets.

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Section: Related Workmentioning

confidence: 99%

Efficient DANNLO Classifier for Multi-class Imbalanced Data on Hadoop

2017

IJMTER

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“…Generally speaking, these methods solved the problem of imbalanced data in the accuracy of minority classes. However, there still exist some drawbacks in these traditional imbalanced data classification methods for handling binary class imbalanced data problems [10]. For example, boosting and bagging based ensemble methods may lose some valuable information in the iteration process owing to the use of sampling methods.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the above problems, we proposed an adaptive ensemble method that is an improvement of existing ensemble method [10]. Our main idea is to transform imbalanced binary problem into multiple balanced problems, which neither reduce the number of majority classes nor increase that of the minority classes.…”

Section: Introductionmentioning

confidence: 99%

“…Then we build multiple base classifiers to deal with these balanced problems and lastly we use an adaptive ensemble rule to assemble the base classification results obtained from base classifiers. Common ensemble rules including Max Rule, Majority Vote Rule, Product Rule, Min Rule, and Sum Rule were proposed in [11] and several novel ensemble rules including MaxDistance Rule, MinDistance Rule, ProDistance Rule, MajDistance Rule, and SumDistance Rule were put forward in [10]. In [10], the test results indicate that their methods have a better performance compared with many conventional imbalanced data processing methods over some standard imbalanced data sets.…”

Section: Introductionmentioning

confidence: 99%

“…Common ensemble rules including Max Rule, Majority Vote Rule, Product Rule, Min Rule, and Sum Rule were proposed in [11] and several novel ensemble rules including MaxDistance Rule, MinDistance Rule, ProDistance Rule, MajDistance Rule, and SumDistance Rule were put forward in [10]. In [10], the test results indicate that their methods have a better performance compared with many conventional imbalanced data processing methods over some standard imbalanced data sets. Meanwhile, the results of their experiments proved that SplitBal + MaxDistance has a better performance than other combinations.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data

Wang¹,

Zhao²,

Gui³

et al. 2017

Scientific Programming

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The class imbalance problems often reduce the classification performance of the majority of standard classifiers. Many methods have been developed to solve these problems, such as cost-sensitive learning methods, synthetic minority oversampling technique (SMOTE), and random oversampling (ROS). However, the existing methods still have some problems due to the possible performance loss of useful information and overfitting. To solve the problems, we propose an adaptive ensemble method by using the most advanced feature of self-adaption by considering an average Euclidean distance between test data and training data, where the average distance is calculated by -nearest neighbors (KNN) algorithm. Simulation results are provided to confirm that the proposed method has a better performance than existing ensemble methods.

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