Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company

Silva, Jesus; Borré, Jenny Romero; Castillo, Aurora Patricia Piñeres; Castro, Ligia Inés García; Varela, Noel

doi:10.1016/j.procs.2019.04.171

Cited by 9 publications

(10 citation statements)

References 10 publications

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“…The classification technique is one of the most implemented data mining tech- Several data mining techniques were used to predict the export abilities of a sample of 272 companies by Silva et al [2]. Synthetic Minority Oversampling Technique (SMOTE) is used to oversample unbalanced data.…”

Section: Methods In Literaturementioning

confidence: 99%

“…The literature presents various problems that were solved by predicting through data mining techniques. In business, DM techniques are used to predict the export abilities of companies [2]. In social media applications, missing link problems between online social networks (OSN) nodes are a frequent problem in which a link is supposed to be between two nodes, but it becomes a missing link for some reasons [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Ba’abbad¹,

Althubiti²,

Alharbi³

et al. 2021

JDAIP

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Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students' records to predict degree completion time.

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Section: Methods In Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Ba’abbad¹,

Althubiti²,

Alharbi³

et al. 2021

JDAIP

View full text Add to dashboard Cite

show abstract

“…Various algorithms were designed for supervised data classification which are based on totally different approaches, for example, neural network [9] and text clustering [10]. Cano in 2019 [11] and Shafiq in 2020 [12] presented good review of data classification methods, including their performance measure and comparison.…”

Section: B Classificationmentioning

confidence: 99%

A New Discretization Approach of Bat and K-Means

Mohamed¹,

Samsudin²

2021

IJACSA

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Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. . The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

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“…Kernels in SVM are used to check for similarities between instances. The formation of the resulting classifier is now generalized enough and can be used for the classification of new samples [5,27].…”

Section: Support Vector Machinementioning

confidence: 99%

A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

Verma¹,

Awasthi²,

Sahu³

2021

RIA

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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 datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

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Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company

Cited by 9 publications

References 10 publications

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

A New Discretization Approach of Bat and K-Means

A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

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