Improving Imbalanced Question Classification Using Structured Smote Based Approach

Mohasseb, Alaa; Bader-El-Den, Mohamed; Cocea, Mihaela; Liu, Han

doi:10.1109/icmlc.2018.8527028

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Most recently, Weng et al [22] utilized SMOTE method and random forests to improve the accuracy of student weariness prediction in education. Mohasseb et al [23] used a hierarchical SMOTE algorithm for balancing different types of questions. Their proposed framework is grammar-based, which involves grammatical pattern for each question and machine learning algorithms to classify patterns.…”

Section: Smote Methodsmentioning

confidence: 99%

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

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With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods.

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Section: Smote Methodsmentioning

confidence: 99%

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

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“…However, there are still a lot of features utilized for categorization, which makes the process time-consuming and inaccurate. Mohasseb et al [ 10 ] proposed a machine learning approach for question classification based on grammar. According to experimental findings, the suggested framework is effective in recognizing various question types and addressing the class imbalance.…”

Section: Related Workmentioning

confidence: 99%

Automatic medical specialty classification based on patients’ description of their symptoms

Mao

Chen

2023

BMC Med Inform Decis Mak

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In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models.

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“…We also research resampling data using SMOTE which will be combined with the algorithm we chose above. SMOTE is one of the most popular techniques used to deal with data imbalance, which helps the minority class to achieve better classifier performance [16] [17].…”

Section: Research Modelmentioning

confidence: 99%

Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm

Bahri¹,

Utami²,

Nasiri³

2022

ccitj

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To improve public services, accuracy and acceleration are needed in classifying the types of complaints so that complaints can immediately get a response from the relevant regional apparatus. This public complaint data is in text form and is not balanced in each category of regional apparatus, so we contribute to research to compare the performance of different text mining-based classification algorithms. In addition, we also tested the resampling method to overcome imbalanced data. In the final stage, testing is carried out using a multiclass confusion matrix table to show accuracy, precision, recall, and f1-score. The test results show the highest value in the Naïve Bayes algorithm with the ComplementNB model without resampling data, which is 89.58% accuracy, 86.72% precision, 82.40% recall, 84.09% f1-score. However, all scores decreased when combined with SMOTE resampling of 83.66% accuracy, 67.79% precision, 80.35% recall, 71.68% f1-score. ComplementNB can be an alternative model in the classification of public complaints with imbalanced datasets

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Improving Imbalanced Question Classification Using Structured Smote Based Approach

Cited by 11 publications

References 22 publications

SMOTE-Boost-based sparse Bayesian model for flood prediction

SMOTE-Boost-based sparse Bayesian model for flood prediction

Automatic medical specialty classification based on patients’ description of their symptoms

Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm

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