Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Asim, Yousra; Raza, Basit; Raza, Basit; Qamar, Nafees

doi:10.1109/access.2020.3048610

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…This article has considered all possible ways to handle unbalanced data sets in different machine learning paradigms. Most of the times, hybrid approaches provide better accuracy while dealing with unbalanced data sets by adopting different corrective measures, as mentioned in some recent studies [39][40][41][42]. AI enabled robust algorithms allowed both the phases of accuracy and estimation to ease out the unbalanced property of the data sets.…”

Section: Discussionmentioning

confidence: 99%

Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Mohindru

Mondal

Banka

2021

CAAI Trans on Intel Tech

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In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real-life solutions using cutting-edge tools and techniques to generate insights into the data. The data-driven machine learning models are now offering more or less worthy results when they are certainly balanced in the input data sets. Imbalanced data occurs when an unequal distribution of classes occurs in the input datasets. Building a predictive model on the imbalanced data set would cause a model that appears to yield high accuracy but does not generalize well to the new data in the minority class. Now the time has come to look into the datasets which are not so-called 'balanced' in nature but such datasets are generally encountered frequently in a workspace. To prevent creating models with false levels of accuracy, the imbalanced data should be rearranged before creating a predictive model. Those data are, sometimes, voluminous, heterogeneous and complex in nature and generate from different autonomous sources with distributed and decentralized control. The driving force is to efficiently handle these data sets using latest tools and techniques for research and commercial insights. The present article provides different such tools and techniques, in different computing frameworks, to handle such Internet of Things and other related datasets to review common techniques for handling imbalanced data in data ecosystems and offers a comparative data modelling framework in Keras for balanced and imbalanced datasets.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Mohindru

Mondal

Banka

2021

CAAI Trans on Intel Tech

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“…In the case of data imbalance, RF may contains a large number of majority classes and a small number of minority in the selected samples when building decision trees, and may favor the majority classes in the final classification vote. Balanced Random Forest (BRF) [ 46 ] combines the ideas of random undersampling and ensembing, where the majority of classes are undersampled and an equal number of minority classes are randomly selected for replacement, as a way to achieve a balanced training set. In the early stage of this study, sampling methods have been used to overcome the problem of data imbalance.…”

Section: Methodsmentioning

confidence: 99%

Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

et al. 2022

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Background Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data. Methods In this paper, a large number of features are first obtained based on $$T_1$$ T 1 WI images using the radiomics methods.Then, we explore the methods of feature selection, sampling and classification, get 17 imbalance machine learning models based on the above features and performed extensive experiments to classify imbalanced soft tissue sarcoma data. Meanwhile, we used another dataset splitting method as well, which could improve the classification performance and verify the validity of the models. Results The experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method. Conclusion Preoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.

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“…Keeping in mind that this research has concentrated on people's views and on determining the impact of the lockdown, this might be relevant in decisionmaking. People use social media sites such as Twitter, Facebook, Instagram, and others to express themselves [8,9]. In terms of health, COVID-19 has posed a threat to everyone's life and health.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

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Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Cited by 8 publications

References 42 publications

Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

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