Evaluation of Machine Learning Frameworks on Bank Marketing and Higgs Datasets

Shashidhara, Bhuvan Malladihalli; Jain, Siddharth; Rao, Vinay; Patil, Nagamma; Raghavendra, G. S.

doi:10.1109/icacce.2015.31

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…For similar problem, Ruangthong and Jaiyen (2015) reached accuracy value of 91.24% in their study, the highest accuracy value reached by Shashidhara et al (2015) was 90.81% and the highest sensitivity value was 94.48%. In the study of Kim et al (2015), who performed Deep Learning application with CNN, the highest accuracy value was reached with 76.70%.…”

Section: Resultsmentioning

confidence: 81%

“…In the analyses performed with Logistic Regression and Neural Networks, it has been observed that Neural Networks performed better. Shashidhara et al (2015) have conducted a study on the most suitable platform for the analysis of marketing data in the banking sector with Machine Learning techniques. In the study, Weka, Scikit Learn, Apache Spark environments have been examined with two different data sets.…”

Section: Background Of the Studymentioning

confidence: 99%

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Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Koçoğlu

Esnaf

2022

International Journal of Business Analytics

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Up to the present, various methods such as Data Mining, Machine Learning, and Artificial Intelligence have been used to get the best assess from huge and important data resource. Deep Learning, one of these methods, is extended version of Artificial Neural Networks. Within the scope of this study, a model has been developed to classify the success of tele-marketing with different machine learning algorithms especially with Deep Learning algorithm. Naïve Bayes, C5.0, Extreme Learning Machine and Deep Learning algorithms have been used for modelling. To examine the effect of class label distribution on model success, Synthetic Minority Oversampling Technique have been used. The results have revealed the success of Deep Learning and Decision Trees algorithms. When the data set was not balanced, the Deep Learning algorithm performed better in terms of sensitivity. Among all models, the best performance in terms of accuracy, precision and F-score have been achieved with the C5.0 algorithm.

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Section: Resultsmentioning

confidence: 81%

Section: Background Of the Studymentioning

confidence: 99%

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Koçoğlu

Esnaf

2022

International Journal of Business Analytics

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“…0.1, 0.01, 0.001, 0.0001). Sklearn is chosen due to its superior performance compare to other machine learning frameworks such as Weka dan Apache Spark [40].…”

Section: Multilayer Perceptron Algorithmmentioning

confidence: 99%

Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing

Halim*¹,

Jaya²,

Fadzil³

2020

IJITEE

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Tele-marketing presents a huge challenge in identifying potential customers with lack of effective marketing strategy may led a company to succumbs to problems such as prolonged marketing campaign. Various attempts to improve the performance of binary classification model for bank tele-marketing data. Previous researches indicate that the neural network is the most common algorithms being employed and able to produce commendable results with higher accuracy percentages compared to other algorithms. Despite several attempts to improve the model through treatment of imbalance dataset and features selection, this research argues that they are incomplete. Therefore, this research proposes a data pre-processing algorithm for bank tele-marketing binary classification neural network. Three datasets have been employed (19, 16, and 20 features) to evaluate the performance of the algorithm towards the classification model. The data pre-processing algorithm is divided into three phases; data cleaning, data imbalance treatment and finally data normalization. In this paper, the result indicated that binary classification model complemented with data cleaning techniques such as Missing common (MC) and Tomek Links (TL) shows a better result compared to Ignore Missing (IM). In terms of data normalization, techniques such as MaxAbsScaler (MAS) and MinMaxScaler (MMS) consistently indicated better performance from other normalization techniques. The classification model employed in this paper utilize data pre-processing algorithm combination of MC-TL-MMS. The algorithm using this approach able to record an area of the receiver operating characteristic curve (AUC) of 0.9129 and 0.9464 by using 16 features and 20 features respectively. This result presents the highest figure in terms of performance accuracy compared to other previous researches

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“…feature engineering). Nevertheless, the best accuracy obtained is 66.93% with RSS (population: 128, generations: 1500, sample: 10000) in 3190.02 s. The best result in[18] is 60.76% realized with logistic regression. RSS vs FSS mean learning time.…”

mentioning

confidence: 96%

Genetic Programming over Spark for Higgs Boson Classification

Hmida

Hamida

Borgi

et al. 2019

Business Information Systems

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With the growing number of available databases having a very large number of records, existing knowledge discovery tools need to be adapted to this shift and new tools need to be created. Genetic Programming (GP) has been proven as an efficient algorithm in particular for classification problems. Notwithstanding, GP is impaired with its computing cost that is more acute with large datasets. This paper, presents how an existing GP implementation (DEAP) can be adapted by distributing evaluations on a Spark cluster. Then, an additional sampling step is applied to fit tiny clusters. Experiments are accomplished on Higgs Boson classification with different settings. They show the benefits of using Spark as parallelization technology for GP. Keywords: Genetic Programming • Machine learning • Spark • Large dataset • Higgs Boson classification 1 'A data lake is a collection of storage instances of various data assets additional to the originating data sources.' (Source: Gartner).

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Evaluation of Machine Learning Frameworks on Bank Marketing and Higgs Datasets

Cited by 12 publications

References 3 publications

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing

Genetic Programming over Spark for Higgs Boson Classification

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