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Статті в журналах з теми "ENSEMBLE LEARNING TECHNIQUE"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

ACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE, and ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 07 (2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.

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This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier's outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models' redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individ

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2

Reddy, S. Pavan Kumar, and U. Sesadri. "A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 8 (2013): 1913–21. http://dx.doi.org/10.24297/ijct.v10i8.1468.

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Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a BSA (Bootstrap Aggregation) is a machine learning ensembleÂ meta-

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3

Goyal, Jyotsana. "IMPROVING CLASSIFICATION PERFORMANCE USING ENSEMBLE LEARNING APPROACH." BSSS Journal of Computer 14, no. 1 (2023): 63–75. http://dx.doi.org/10.51767/jc1409.

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The data mining techniques are used for evaluation of the data in order to find and represent the data in such manner by which the applications are becomes beneficial. Therefore, different kinds of computational algorithms and modeling’s are incorporated for analyzing the data. These computational algorithms are help to understand the data patterns and their application utility. The data mining algorithms supports supervised as well as unsupervised techniques of data analysis. This work is aimed to investigate about the supervised learning technique specifically performance improvements on cla

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4

Cawood, Pieter, and Terence Van Zyl. "Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion." Forecasting 4, no. 3 (2022): 732–51. http://dx.doi.org/10.3390/forecast4030040.

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The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Ba

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5

Lenin, Thingbaijam, and N. Chandrasekaran. "Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms." Webology 18, Special Issue 01 (2021): 183–95. http://dx.doi.org/10.14704/web/v18si01/web18053.

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Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random

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6

Arora, Madhur, Sanjay Agrawal, and Ravindra Patel. "Machine Learning Technique for Predicting Location." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 639–45. http://dx.doi.org/10.37391/ijeer.110254.

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In the current era of internet and mobile phone usage, the prediction of a person's location at a specific moment has become a subject of great interest among researchers. As a result, there has been a growing focus on developing more effective techniques to accurately identify the precise location of a user at a given instant in time. The quality of GPS data plays a crucial role in obtaining high-quality results. Numerous algorithms are available that leverage user movement patterns and historical data for this purpose. This research presents a location prediction model that incorporates data

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7

Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "An Ensemble Machine Learning Technique for Functional Requirement Classification." Symmetry 12, no. 10 (2020): 1601. http://dx.doi.org/10.3390/sym12101601.

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In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifyin

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8

., Hartono, Opim Salim Sitompul, Erna Budhiarti Nababan, Tulus ., Dahlan Abdullah, and Ansari Saleh Ahmar. "A New Diversity Technique for Imbalance Learning Ensembles." International Journal of Engineering & Technology 7, no. 2.14 (2018): 478. http://dx.doi.org/10.14419/ijet.v7i2.11251.

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Data mining and machine learning techniques designed to solve classification problems require balanced class distribution. However, in reality sometimes the classification of datasets indicates the existence of a class represented by a large number of instances whereas there are classes with far fewer instances. This problem is known as the class imbalance problem. Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. An ideal ensemble system should have accurrate individual classifiers and if there is an err

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9

Teoh, Chin-Wei, Sin-Ban Ho, Khairi Shazwan Dollmat, and Chuie-Hong Tan. "Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning." International Journal of Information and Education Technology 12, no. 8 (2022): 741–45. http://dx.doi.org/10.18178/ijiet.2022.12.8.1679.

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Анотація:

The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era has promoted the rise of the big data era in educational data. This situation has created an opportunity for an educator to utilize the available data from MOOCs to facilitate student learning and performance. Therefore, this research study aims to introduce three types of ensemble learning methods, which are stacking, boosting, and bagging, to predict student performance. These techniques combine the advantage of feature selection method and Synthetic Minority Oversampling Techn

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10

Hussein, Salam Allawi, Alyaa Abduljawad Mahmood, and Emaan Oudah Oraby. "Network Intrusion Detection System Using Ensemble Learning Approaches." Webology 18, SI05 (2021): 962–74. http://dx.doi.org/10.14704/web/v18si05/web18274.

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To mitigate modern network intruders in a rapidly growing and fast pattern changing network traffic data, single classifier is not sufficient. In this study Chi-Square feature selection technique is used to select the most important features of network traffic data, then AdaBoost, Random Forest (RF), and XGBoost ensemble classifiers were used to classify data based on binary-classes and multi-classes. The aim of this study is to improve detection rate accuracy for every individual attack types and all types of attacks, which will help us to identify attacks and particular category of attacks.

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11

P A, Sadiyamole, and Dr Manju Priya S. "Heart Disease Prediction Using Ensemble Stacking Technique." International Journal of Engineering Research in Computer Science and Engineering 9, no. 8 (2022): 19–24. http://dx.doi.org/10.36647/ijercse/09.08.art004.

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Heart disease is one of the critical reasons behind the majority of the human loss.Heart failure has proven as the major health issue in both men and women.This causes human life very dreadful.Diagnosing heart issues in advance is a tedious task as it requires enormous amount of clinical tests.Data mining techniques like machine learning and deep learning have proven to be fruitful in making decisions and diagnose various diseases in advance.In this paper,various machine learning techniques have been used along with stacking ensemble method that focus to improve the prediction of heart failure

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12

Zubair Khan, Mohammad. "Hybrid Ensemble Learning Technique for Software Defect Prediction." International Journal of Modern Education and Computer Science 12, no. 1 (2020): 1–10. http://dx.doi.org/10.5815/ijmecs.2020.01.01.

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13

Pandey, Hemakshi, Riya Goyal, Deepali Virmani, and Charu Gupta. "Ensem_SLDR: Classification of Cybercrime using Ensemble Learning Technique." International Journal of Computer Network and Information Security 14, no. 1 (2021): 81–90. http://dx.doi.org/10.5815/ijcnis.2022.01.07.

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With the advancement of technology, cybercrimes are surging at an alarming rate as miscreants pour into the world's modern reliance on the virtual platform. Due to the accumulation of an enormous quantity of cybercrime data, there is huge potential to analyze and segregate the data with the help of Machine Learning. The focus of this research is to construct a model, Ensem_SLDR which can predict the relevant sections of IT Act 2000 from the compliant text/subjects with the aid of Natural Language Processing, Machine Learning, and Ensemble Learning methods. The objective of this paper is to imp

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14

Al Duhayyim, Mesfer, Sidra Abbas, Abdullah Al Hejaili, Natalia Kryvinska, Ahmad Almadhor, and Uzma Ghulam Mohammad. "An Ensemble Machine Learning Technique for Stroke Prognosis." Computer Systems Science and Engineering 47, no. 1 (2023): 413–29. http://dx.doi.org/10.32604/csse.2023.037127.

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15

Chandra Jena, Prakash, Subhendu Kumar Pani, and Debahuti Mishra. "A novel approach to ensemble learning in distributed data mining." International Journal of Engineering & Technology 7, no. 3.3 (2018): 233. http://dx.doi.org/10.14419/ijet.v7i2.33.14159.

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Several data mining techniques have been proposed to take out hidden information from databases. Data mining and knowledge extraction becomes challenging when data is massive, distributed and heterogeneous. Classification is an extensively applied task in data mining for prediction. Huge numbers of machine learning techniques have been developed for the purpose. Ensemble learning merges multiple base classifiers to improve the performance of individual classification algorithms. In particular, ensemble learning plays a significant role in distributed data mining. So, study of ensemble learning

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16

Dhanwanth, Batini, Bandi Vivek, M. Abirami, Shaik Mohammad Waseem, and Challapalli Manikantaa. "Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 336–44. http://dx.doi.org/10.17762/ijritcc.v11i5s.7035.

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India is a rapidly expanding nation on a global scale. Chronic kidney disease (CKD) is a prevalent health problem internationally, and advance perception of this disease can aid prevent its stream. This research proposes an ensemble learning technique that combines three different algorithms, Logistic Regression, Gradient Boosting and Random Forest for the prediction of CKD. The performance of each algorithm was judged based on Root Mean Square Error (RMSE) and Mean Square Error (MSE) as performance metrics, and the predictions of each algorithm were combined using an ensemble learning techniq

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17

Sun, Xiao Wei, and Hong Bo Zhou. "Research on Applied Technology in Experiments with Three Boosting Algorithms." Advanced Materials Research 908 (March 2014): 513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.908.513.

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Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. In this paper we use applied technology to built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN a

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18

Liu, Rencheng, Saqib Ali, Syed Fakhar Bilal, et al. "An Intelligent Hybrid Scheme for Customer Churn Prediction Integrating Clustering and Classification Algorithms." Applied Sciences 12, no. 18 (2022): 9355. http://dx.doi.org/10.3390/app12189355.

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Nowadays, customer churn has been reflected as one of the main concerns in the processes of the telecom sector, as it affects the revenue directly. Telecom companies are looking to design novel methods to identify the potential customer to churn. Hence, it requires suitable systems to overcome the growing churn challenge. Recently, integrating different clustering and classification models to develop hybrid learners (ensembles) has gained wide acceptance. Ensembles are getting better approval in the domain of big data since they have supposedly achieved excellent predictions as compared to sin

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19

Shah, Shariq, Hossein Ghomeshi, Edlira Vakaj, Emmett Cooper, and Rasheed Mohammad. "An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis." Big Data and Cognitive Computing 7, no. 2 (2023): 85. http://dx.doi.org/10.3390/bdcc7020085.

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Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans exp

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20

Vacchetti, Bartolomeo, and Tania Cerquitelli. "Cinematographic Shot Classification with Deep Ensemble Learning." Electronics 11, no. 10 (2022): 1570. http://dx.doi.org/10.3390/electronics11101570.

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Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and ex

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21

Cui, Su, Yiliang Han, Yifei Duan, Yu Li, Shuaishuai Zhu, and Chaoyue Song. "A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification." Entropy 25, no. 4 (2023): 555. http://dx.doi.org/10.3390/e25040555.

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In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the

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22

Troć, Maciej, and Olgierd Unold. "Self-adaptation of parameters in a learning classifier system ensemble machine." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 157–74. http://dx.doi.org/10.2478/v10006-010-0012-8.

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Self-adaptation of parameters in a learning classifier system ensemble machineSelf-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This wo

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23

Chandrasekar, Jayakumar, Surendar Madhawa, and J. Sangeetha. "Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8365–76. http://dx.doi.org/10.3233/jifs-189155.

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A robust disruption prediction system is mandatory in a Tokamak control system as the disruption can cause malfunctioning of the plasma-facing components and impair irrecoverable structural damage to the vessel. To mitigate the disruption, in this article, a data-driven based disruption predictor is developed using an ensemble technique. The ensemble algorithm classifies disruptive and non-disruptive discharges in the GOLEM Tokamak system. Ensemble classifiers combine the predictive capacity of several weak learners to produce a single predictive model and are utilized both in supervised and u

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24

Rhmann, Wasiur. "An Ensemble of Hybrid Search-Based Algorithms for Software Effort Prediction." International Journal of Software Science and Computational Intelligence 13, no. 3 (2021): 28–37. http://dx.doi.org/10.4018/ijssci.2021070103.

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Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-

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25

Li, Xingjian, Haoyi Xiong, Zeyu Chen, Jun Huan, Cheng-Zhong Xu, and Dejing Dou. "“In-Network Ensemble”: Deep Ensemble Learning with Diversified Knowledge Distillation." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (2021): 1–19. http://dx.doi.org/10.1145/3473464.

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Ensemble learning is a widely used technique to train deep convolutional neural networks (CNNs) for improved robustness and accuracy. While existing algorithms usually first train multiple diversified networks and then assemble these networks as an aggregated classifier, we propose a novel learning paradigm, namely, “In-Network Ensemble” ( INE ) that incorporates the diversity of multiple models through training a SINGLE deep neural network. Specifically, INE segments the outputs of the CNN into multiple independent classifiers, where each classifier is further fine-tuned with better accuracy

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26

Adamu, Yusuf Aliyu. "MALARIA PREDICTION MODEL USING ADVANCED ENSEMBLE MACHINE LEARNING TECHNIQUES." Journal of Medical pharmaceutical and allied sciences 10, no. 6 (2021): 3794–801. http://dx.doi.org/10.22270/jmpas.v10i6.1701.

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Malaria is a life-threatening disease that leads to death globally, its early prediction is necessary for preventing the rapid transmission. In this work, an enhanced ensemble learning approach for predicting malaria outbreaks is suggested. Using a mean-based splitting strategy, the dataset is randomly partitioned into smaller groups. The splits are then modelled using a classification and regression tree, and an accuracy-based weighted aging classifier ensemble is used to construct a homogenous ensemble from the several Classification and Regression Tree models. This approach ensures higher p

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27

Ferano, Francisco Calvin Arnel, Amalia Zahra, and Gede Putra Kusuma. "Stacking ensemble learning for optical music recognition." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 3095–104. http://dx.doi.org/10.11591/eei.v12i5.5129.

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The development of music culture has resulted in a problem called optical music recognition (OMR). OMR is a task in computer vision that explores the algorithms and models to recognize musical notation. This study proposed the stacking ensemble learning model to complete the OMR task using the common western musical notation (CWMN) musical notation. The ensemble learning model used four deep convolutional neural networks (DCNNs) models, namely ResNeXt50, Inception-V3, RegNetY-400MF, and EfficientNet-V2-S as the base classifier. This study also analysed the most appropriate technique to be used

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28

Christianah, Abikoye Oluwakemi, Benjamin Aruwa Gyunka, and Akande Noah Oluwatobi. "Optimizing Android Malware Detection Via Ensemble Learning." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 09 (2020): 61. http://dx.doi.org/10.3991/ijim.v14i09.11548.

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<p>Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis avoidance techniques such that the traditional signature-based detection methods have become less potent in their ability to detect new and unknown malware. Alternative approaches, such as the Machine learning techniqu

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29

Munsarif, Muhammad, Muhammad Sam’an, and Safuan Safuan. "Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3483–89. http://dx.doi.org/10.11591/eei.v11i6.3927.

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Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and deci

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30

Hamori, Hitoshi, and Shigeyuki Hamori. "Does Ensemble Learning Always Lead to Better Forecasts?" Applied Economics and Finance 7, no. 2 (2020): 51. http://dx.doi.org/10.11114/aef.v7i2.4716.

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Ensemble learning is a common machine learning technique applied to business and economic analysis in which several classifiers are combined using majority voting for better forecasts as compared to those of individual classifier. This study presents a counterexample, which demonstrates that ensemble learning leads to worse classifications than those from individual classifiers, using two events and three classifiers. If there is an outstanding classifier, we should follow its forecast instead of using ensemble learning.

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Mahajan, Palak, Shahadat Uddin, Farshid Hajati, and Mohammad Ali Moni. "Ensemble Learning for Disease Prediction: A Review." Healthcare 11, no. 12 (2023): 1808. http://dx.doi.org/10.3390/healthcare11121808.

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Анотація:

Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting,

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32

Sarkar, Nipa, and Asha Rani Borah. "Predicting ESRD Risk via Supervised and Ensemble Machine Learning Technique." International Journal of Research in Advent Technology 7, no. 4 (2019): 173–77. http://dx.doi.org/10.32622/ijrat.74201970.

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Lee, Yen-Hsien, Paul Jen-Hwa Hu, Tsang-Hsiang Cheng, Te-Chia Huang, and Wei-Yao Chuang. "A preclustering-based ensemble learning technique for acute appendicitis diagnoses." Artificial Intelligence in Medicine 58, no. 2 (2013): 115–24. http://dx.doi.org/10.1016/j.artmed.2013.03.007.

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Alruily, Meshrif, Sameh Abd El-Ghany, Ayman Mohamed Mostafa, Mohamed Ezz, and A. A. Abd El-Aziz. "A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction." Applied Sciences 13, no. 8 (2023): 5047. http://dx.doi.org/10.3390/app13085047.

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A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were

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Krasnopolsky, Vladimir M., and Ying Lin. "A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US." Advances in Meteorology 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/649450.

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A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and to produce “optimal” forecast represented by a nonlinear NN ensemble mean. The NN approach is compared with the conservative multi-model ensemble, with multiple linear regression ensemble approaches, and with results obtained by human forecasters. The NN multi-model ensemble improves upo

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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms." Revue d'Intelligence Artificielle 35, no. 3 (2021): 209–15. http://dx.doi.org/10.18280/ria.350304.

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Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed

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37

Devi, Debashree, Suyel Namasudra, and Seifedine Kadry. "A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem." International Journal of Data Warehousing and Mining 16, no. 3 (2020): 60–86. http://dx.doi.org/10.4018/ijdwm.2020070104.

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The subject of a class imbalance is a well-investigated topic which addresses performance degradation of standard learning models due to uneven distribution of classes in a dataspace. Cluster-based undersampling is a popular solution in the domain which offers to eliminate majority class instances from a definite number of clusters to balance the training data. However, distance-based elimination of instances often got affected by the underlying data distribution. Recently, ensemble learning techniques have emerged as effective solution due to its weighted learning principle of rare instances.

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Salunkhe, Uma R., and Suresh N. Mali. "Security Enrichment in Intrusion Detection System Using Classifier Ensemble." Journal of Electrical and Computer Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1794849.

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In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines

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Tsai, Chih-Fong, and Chihli Hung. "Modeling credit scoring using neural network ensembles." Kybernetes 43, no. 7 (2014): 1114–23. http://dx.doi.org/10.1108/k-01-2014-0016.

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Purpose – Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving this type of problem, and advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, provide better prediction performance than single machine learning based classification techniques. However, it is not known which type of advanced classification technique performs better in terms of financial dist

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Tang, Ling, Wei Dai, Lean Yu, and Shouyang Wang. "A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting." International Journal of Information Technology & Decision Making 14, no. 01 (2015): 141–69. http://dx.doi.org/10.1142/s0219622015400015.

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To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of c

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Ali, Abdullah Marish, Fuad A. Ghaleb, Bander Ali Saleh Al-Rimy, Fawaz Jaber Alsolami, and Asif Irshad Khan. "Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique." Sensors 22, no. 18 (2022): 6970. http://dx.doi.org/10.3390/s22186970.

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Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short,

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Namoun, Abdallah, Burhan Rashid Hussein, Ali Tufail, Ahmed Alrehaili, Toqeer Ali Syed, and Oussama BenRhouma. "An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation." Sensors 22, no. 9 (2022): 3506. http://dx.doi.org/10.3390/s22093506.

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With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed

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Li, Kai, and Hong Tao Gao. "A Subgraph-Based Selective Classifier Ensemble Algorithm." Advanced Materials Research 219-220 (March 2011): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.261.

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To improve the generalization performance for ensemble learning, a subgraph based selective classifier ensemble algorithm is presented. Firstly, a set of classifiers are generated by bootstrap sampling technique and support vector machine learning algorithm. And a complete undirected graph is constructed whose vertex is classifier and weight of edge between a pair of classifiers is diversity values. Secondly, by searching technique to find an edge with minimum weight and to calculate similarity values about two vertexes which is related to the edge, vertex with smaller similarity value is remo

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Srınıvasa Rao, B. "A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases." E3S Web of Conferences 309 (2021): 01007. http://dx.doi.org/10.1051/e3sconf/202130901007.

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The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.

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Hashim, Dhurgham Kadhim, and Lamia Abed Noor Muhammed. "Performance of K-means algorithm based an ensemble learning." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 575–80. http://dx.doi.org/10.11591/eei.v11i1.3550.

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K-means is an iterative algorithm used with clustering task. It has more characteristics such as simplicity. In the same time, it suffers from some of drawbacks, sensitivity to initial centroid values that may produce bad results, they are based on the initial centroids of clusters that would be selected randomly. More suggestions have been given in order to overcome this problem. Ensemble learning is a method used in clustering; multiple runs are executed that produce different results for the same data set. Then the final results are driven. According to this hypothesis, more ensemble learni

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Liu, Kun-Hong, Muchenxuan Tong, Shu-Tong Xie, and Vincent To Yee Ng. "Genetic Programming Based Ensemble System for Microarray Data Classification." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/193406.

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Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase

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Ahmed, Kanwal, Muhammad Imran Nadeem, Dun Li, et al. "Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis." Symmetry 15, no. 3 (2023): 645. http://dx.doi.org/10.3390/sym15030645.

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The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers

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Ali, Muhammad Danish, Adnan Saleem, Hubaib Elahi, et al. "Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks." Diagnostics 13, no. 13 (2023): 2242. http://dx.doi.org/10.3390/diagnostics13132242.

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This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the pr

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Shamsuddin, Siti Nurasyikin, Noriszura Ismail, and R. Nur-Firyal. "Life Insurance Prediction and Its Sustainability Using Machine Learning Approach." Sustainability 15, no. 13 (2023): 10737. http://dx.doi.org/10.3390/su151310737.

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Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provi

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Tama, Bayu Adhi, and Marco Comuzzi. "Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs." Electronics 11, no. 16 (2022): 2548. http://dx.doi.org/10.3390/electronics11162548.

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Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article,

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