Performance Evaluation of Naive Bayes Classifier with and without Filter Based Feature Selection

Prabha, Dr.D.; Subramanian, Mr. R. Siva; Balakrishnan, Dr.S.; Karpagam, Dr.M.

doi:10.35940/ijitee.j9376.0881019

Cited by 15 publications

(3 citation statements)

References 20 publications

(21 reference statements)

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“…To understand the efficiency of the model the empirical results captured from the methodology is presented & projected using different metrics like: accuracy, AUC, recall and precision [33,34].…”

Section: 2data Pre-processingmentioning

confidence: 99%

Customer churn prediction model in enterprises using machine learning

2024

IJATEE

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The business of any enterprises is highly contingent upon their consumers. Understanding how the customers perform with the enterprises business is crucial one. Since the firm's business profit is based upon the customer satisfaction. To comprehend how the customer relationship with enterprises business can be analysed using the customer churn prediction (CCP). The term customer churn refers to frequency at which the client cease doing the business with the enterprises [1]. *Author for correspondenceThe customer churn can be measured using no product purchased during the time of interval by the consumer. Customer churn is significant issue in any enterprises around the globe.Due to numerous factors contributing to customer churn, enterprises often face adverse effects, including revenue loss and the need to invest significant resources in acquiring new customers. The primary causes of customer churn include: Attracting the wrong customers.  Mismatch between product offerings and customer needs.

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Section: 2data Pre-processingmentioning

confidence: 99%

Customer churn prediction model in enterprises using machine learning

2024

IJATEE

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“…This measure is calculated according to the equation P (X, Y ) = Cov(x,y) σ X σ Y , where X is the input attribute and Y is the class. The equation uses the covariance between them (Cov(x, y)) and the standard deviation of each of them σ X and σ Y [Prabha et al 2019].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Albuquerque

Tarrataca

Brandão

et al. 2022

jidm

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Educational Data Mining is an interdisciplinary field that helps understand educational phenomena through computational techniques. The databases of educational institutions are usually extensive, possessing many descriptive attributes that make the prediction process complex. In addition, the data can be sparse, redundant, irrelevant, and noisy, which can degrade the predictive quality of the models and affect computational performance. One way to simplify the problem is to identify the least important attributes and omit them from the modeling process. This can be performed by employing attribute selection techniques. This work evaluates different feature selection techniques applied to open educational data and paired alongside a genetic algorithm with a flexible fitness function. The methods and results described herein extend a previously published paper by: (i) describing a larger set of computational experiments; (ii) performing a hypothesis test over different classifiers; and (iii) presenting a more in-depth literature revision. The results obtained indicate an improvement in the classification process.

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“…The findings that are obtained using a straightforward FS method like a filter, notwithstanding their speed and effectiveness, are ineffective. Filter FS has several benefits, the most important of which are that it is 1) independent of the model being used, 2) reliant on the features being used, 3) computationally efficient, and 4) uses statistical approaches [2]. The filter FS does not take into account the feature dependencies, which is one of its disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble Variable Selection To Improve Customer Prediction Using Naive Bayes Model

R¹,

Girija

Anuradha³

et al. 2023

IJRITCC

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The analysis of customer patterns and behaviours is essential for all businesses, as the customer is the sole source of revenue. Understanding customer patterns and behavior enables businesses to enhance their business processes and customer happiness. The availability of voluminous client datasets within organizations facilitates efficient customer analysis. Yet, the inclusion of interrelated, irrelevant, as well as missing factors leads to a poor forecast of the dataset. Feature selection techniques are investigated in order to handle the problem. Objective of feature selection is to pick the pertinent variables from a complete set of associated, irrelevant, and missing variables. In general, FS is classified into 3 types: filter, wrapper, & hybrid method. The filter method is a quick one, but the variables used are ineffective. Similarly, a wrapper method is effective yet computationally inefficient. In this study, an ensemble feature selection strategy is presented and tested to circumvent the issue with these feature selections. There are two techniques to ensemble FS: one is homogenous and the other is heterogeneous. This study employs a heterogeneous ensemble feature selection method. In the suggested method, the learning dataset is applied to five distinct filter FS approaches, and the ranked attributes that result are aggregated using two distinct methods: the mean method and the min method. Relevant variables are chosen to further build the final sorted qualities using the cut off value as a guide. As the HEVS technique's filter approach simply ranks the variables, it is necessary to select the variable subset cut off value. The experimental technique is conducted from two distinct vantage points: Heterogeneous ensemble variable selection with Naive Bayes and Naive Bayes without variable selection. In the end, the outcomes that were obtained via the use of the two different approaches are compared using different factors. The experimental results demonstrate that the suggested HEVS method outperforms the usual Naive Bayes model. As relevant variables are included when modeling using NB, the computational complexity of this proposed methodology is also minimized.

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Performance Evaluation of Naive Bayes Classifier with and without Filter Based Feature Selection

Cited by 15 publications

References 20 publications

Customer churn prediction model in enterprises using machine learning

Customer churn prediction model in enterprises using machine learning

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Heterogeneous Ensemble Variable Selection To Improve Customer Prediction Using Naive Bayes Model

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