Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning

Muslim, Much Aziz; Dasril, Yosza

doi:10.11591/ijece.v11i6.pp5549-5557

Cited by 27 publications

(15 citation statements)

References 34 publications

(38 reference statements)

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“…In fact, the emotion detection task and polarity detection task are associated with each other, and in most cases, detection of the emotion represents a sub-function from the detection of the polarity. For the more advanced analysis task, polarity detection can be used as a subtask to classify customers' reviews whether they like or dislike services [38], [39]. This helps in judging the quality of the products [2], [5], [40], [41].…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Alabid

Katheeth

2021

IJEECS

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A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.

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Section: Related Workmentioning

confidence: 99%

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Alabid

Katheeth

2021

IJEECS

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“…This competition arises due to the market saturation of abundant service providers and the products' offers diversity. Herein, churn prediction is a business use case, which applies various data mining techniques to detect the customers who are likely to cancel their subscription to a special service [5], [6]. Customer behavior changes in line with the defined business use case.…”

Section: Introductionmentioning

confidence: 99%

Smart evaluation for deep learning model: churn prediction as a product case study

Elgohary¹,

Galal

Mosa

et al. 2023

Bulletin EEI

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Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.

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“…XGBoost is the improved version with the introduction of regularization terms as well as second-order derivatives. These solutions have been applied for various problems like business [42], finance [30], the industry [35], and last but not least security [72].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

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Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning

Cited by 27 publications

References 34 publications

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Smart evaluation for deep learning model: churn prediction as a product case study

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

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