scholarly journals A Novel Model for Imbalanced Data Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6680-6687
Author(s):  
Jian Yin ◽  
Chunjing Gan ◽  
Kaiqi Zhao ◽  
Xuan Lin ◽  
Zhe Quan ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Performance ◽  
Classification Model ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Public Datasets ◽  
Distribution Cost ◽  
Novel Model ◽  
Learning Data

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

scholarly journals A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Baofeng Shi ◽  
Jing Wang ◽  
Junyan Qi ◽  
Yanqiu Cheng
Keyword(s):  
Logistic Regression ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Approach ◽  
Fisher Discriminant ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Customer Classification ◽  
Key Indicators ◽  
Rating Model

We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

Imbalanced Data Classification Algorithm Based on Clustering and SVM

2020 ◽  
pp. 2150036
Author(s):  
Bo Huang ◽  
Yimin Zhu ◽  
Zhongzhen Wang ◽  
Zhijun Fang
Keyword(s):  
Class Imbalance ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Algorithm ◽  
Classification Model ◽  
Imbalance Data ◽  
Imbalance Problem ◽  
Imbalanced Data Classification ◽  
Under Sampling ◽  
Feature Dimension

The class-imbalance learning is one of the most significant research topics in the data mining and machine learning. Imbalance problem means that one of the classes has much more samples than that of other classes. To deal with the issues of low classification accuracy and high time complexity, this paper proposes an novel imbalance data classification algorithm based on clustering and SVM. The algorithm suggests under-sampling in majority samples based on the distribution characteristics of minority samples. First, specific clusters are detected by cluster analysis on the minority. Second, a cluster boundary strategy is proposed to eliminate the bad influence of noise samples. To structure a balanced dataset for imbalance data, this paper proposes three principles of under-sampling on majority samples according to the characteristic of samples in the cluster. Finally, the optimal classification model from the linear combination of hybrid-kernel SVM is obtained. The experiments based on datasets in UCI and KEEL database show that our algorithm effectively decreases the interference of noise samples. Compared with the SMOTE and Fast-CBUS, the proposed algorithm not only reduces the feature dimension, but also improves the precision of the minor classes under the different labeled sample rates generally.

scholarly journals Multi-Domain Sentiment Classification Based on Domain-Aware Embedding and Attention

2019 ◽  
Author(s):  
Yitao Cai ◽  
Xiaojun Wan
Keyword(s):  
Classification Performance ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Word Embeddings ◽  
Classification Models ◽  
Domain Specific ◽  
Proposed Model ◽  
Public Datasets ◽  
Form Domain

Sentiment classification is a fundamental task in NLP. However, as revealed by many researches, sentiment classification models are highly domain-dependent. It is worth investigating to leverage data from different domains to improve the classification performance in each domain. In this work, we propose a novel completely-shared multi-domain neural sentiment classification model to learn domain-aware word embeddings and make use of domain-aware attention mechanism. Our model first utilizes BiLSTM for domain classification and extracts domain-specific features for words, which are then combined with general word embeddings to form domain-aware word embeddings. Domain-aware word embeddings are fed into another BiLSTM to extract sentence features. The domain-aware attention mechanism is used for selecting significant features, by using the domain-aware sentence representation as the query vector. Evaluation results on public datasets with 16 different domains demonstrate the efficacy of our proposed model. Further experiments show the generalization ability and the transferability of our model.

scholarly journals RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification

2021 ◽  
Author(s):  
Michał Koziarski ◽  
Colin Bellinger ◽  
Michał Woźniak
Keyword(s):  
Binary Data ◽  
State Of The Art ◽  
Input Parameter ◽  
Health And Safety ◽  
Imbalanced Data ◽  
Classification Performance ◽  
The State ◽  
Training Data ◽  
Classification Model ◽  
Imbalanced Data Classification

AbstractReal-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such cases, the classification model must achieve a high recall without significantly impacting precision. Resampling the training data is the standard approach to improving classification performance on imbalanced binary data. However, the state-of-the-art methods ignore the local joint distribution of the data or correct it as a post-processing step. This can causes sub-optimal shifts in the training distribution, particularly when the target data distribution is complex. In this paper, we propose Radial-Based Combined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class potential to refine the energy-based resampling approach of CCR. In particular, RB-CCR exploits the class potential to accurately locate sub-regions of the data-space for synthetic oversampling. The category sub-region for oversampling can be specified as an input parameter to meet domain-specific needs or be automatically selected via cross-validation. Our $$5\times 2$$ 5 × 2 cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.

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