Principal Component Analysis on Mixed Data For Deep Neural Network Classifier in Banking System

Krishna, Chittem Leela; Reddy, Poli Venkata Subba

doi:10.26438/ijcse/v7i5.129134

Cited by 3 publications

(16 citation statements)

References 5 publications

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“…Data reduction techniques can be applied in attaining a subset of the original data such that the results obtained on the subset are similar to that of the results of original data. In this paper, we have focused on two data reduction techniques, namely Attribute subset selection, a feature selection method [1,2] and Principal component analysis, a feature extraction method [2,15].…”

Section: Data Reductionmentioning

confidence: 99%

“…The process of selecting a subset of relevant attributes, namely dimensions/features by removing redundant or weak attributes from the input data set is termed as Attribute subset selection [1,2]. Among all the possible subsets of attributes, the best subset is chosen by applying one of the below heuristics: Forward Selection -Determine the best attribute among all and add it to the empty set and repeat the process iteratively until the reduced set of attributes is obtained.…”

Section: A Attribute Subset Selectionmentioning

confidence: 99%

“…Principal Component Analysis (PCA) is a lossy dimensionality reduction technique, where an approximation of the original data can be reconstructed from its reduced data. PCA makes use of data encoding techniques and searches among the n attributes of the original data set to find k orthogonal vectors [2,15]. A reduced data set with k dimensions is projected onto a new space such that k ≤ n. The basic process of PCA is given below: 1.…”

Section: B Principal Component Analysismentioning

confidence: 99%

“…A Multi-layered neural network has one input, one or more hidden and one output layers in which each neuron in layer i is interconnected to all the neurons in layer i+1 through the weights associating them. This work focuses on neural network based classification using Backpropagation [1,15,22]. Once the training data is supplied to the neural network, the learning phase deals with finalizing certain network parameters such as updating the weights and minimizing the error function.…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

“…Naïve Bayesian classifier is built based on the assumption that an attribute value is independent on the values of other attributes for each class label. Bayes' theorem defines the posterior probability of H conditioned on T, P(H|T), for some hypothesis H and a tuple T of the input data based on (1). Once the classifier is built, posterior probabilities are evaluated for each tuple D in the test data conditioned on every possible class.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Krishna¹,

Reddy²

2019

IJRTE

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The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the realworld domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy

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Section: Data Reductionmentioning

confidence: 99%

Section: A Attribute Subset Selectionmentioning

confidence: 99%

Section: B Principal Component Analysismentioning

confidence: 99%

Section: Backpropagation Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Krishna¹,

Reddy²

2019

IJRTE

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Predictive Model using SVM to Improve the Effectiveness of Direct Marketing

Sagala

Permai

2021

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)

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Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

Setyarini,

Gayatri,

Aditya

et al. 2024

matrik

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A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.

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Principal Component Analysis on Mixed Data For Deep Neural Network Classifier in Banking System

Cited by 3 publications

References 5 publications

Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Predictive Model using SVM to Improve the Effectiveness of Direct Marketing

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

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