Hybrid Feature Selection Approach for Naive Bayes to Improve Consumer Behavior Analysis

2021 ◽  
Author(s):  
B. Maheswari ◽  
J Aswini ◽  
M. Anita
Keyword(s):  
Feature Selection ◽  
Consumer Behavior ◽  
Behavior Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Consumer Behavior Analysis ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals A Consumer Behavior Prediction Method for E-Commerce Application

2019 ◽  
Vol 8 (2S6) ◽  
pp. 983-988
Keyword(s):  
Consumer Behavior ◽  
Behavior Analysis ◽  
Naive Bayes ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Customer Behavior ◽  
Behavior Prediction ◽  
Bayes Method ◽  
Hybrid Classifier ◽  
Consumer Behavior Analysis

The consumer behavior analysis is the technique which is applied to analyze consumer behavior. The customer behavior analysis has the three steps which are pre-processing, feature extraction and classification for prediction. In the previous work, Naïve Bayes was applied for the consumer behavior analysis. In this work, hybrid classifier is designed for the customer behavior analysis using Decision Tree and KNN. The proposed method is implemented in anaconda python and results are compared with the previously used Naïve Bayes method, for this analysis consumer reviews from Amazon website are used.

A New Feature Selection Approach to Naive Bayes Text Classifiers

2016 ◽  
Vol 30 (02) ◽  
pp. 1650003 ◽  
Author(s):  
Lungan Zhang ◽  
Liangxiao Jiang ◽  
Chaoqun Li
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
High Dimensional ◽  
Text Data ◽  
Selection Approach ◽  
Search Feature ◽  
Text Classifiers ◽  
New Feature ◽  
Feature Selection Approach

Handling text data is a challenge for machine learning because text data is high dimensional in many cases. Feature selection has been approved to be an effective approach to handle high-dimensional data. Feature selection approaches can be broadly divided into two categories: filter approaches and wrapper approaches. Generally, wrapper approaches have superior accuracy compared to filters, but filters always run faster than wrapper approaches. In order to integrate the advantages of filter approaches and wrapper approaches, we propose a gain ratio-based hybrid feature selection approach to naive Bayes text classifiers. The hybrid feature selection approach uses base classifiers to evaluate feature subsets like wrapper approaches, but it need not repeatedly search feature subsets and build base classifiers. The experimental results on large suite of benchmark text datasets show that the proposed hybrid feature selection approach significantly improves the classification accuracy of the original naive Bayes text classifiers while does not incur the high time complexity that characterizes wrapper approaches.

scholarly journals Feature Selection Approach to Detect DDoS Attack Using Machine Learning Algorithms

2021 ◽  
Vol 5 (4) ◽  
pp. 395
Author(s):  
Muhammad Aqil Haqeemi Azmi ◽  
Cik Feresa Mohd Foozy ◽  
Khairul Amin Mohamad Sukri ◽  
Nurul Azma Abdullah ◽  
Isredza Rahmi A. Hamid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Naive Bayes ◽  
Past Research ◽  
Decision Table ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
The Past ◽  
Selection Approach ◽  
Feature Selection Approach

Distributed Denial of Service (DDoS) attacks are dangerous attacks that can cause disruption to server, system or application layer. It will flood the target server with the amount of Internet traffic that the server could not afford at one time. Therefore, it is possible that the server will not work if it is affected by this DDoS attack. Due to this attack, the network security environment becomes insecure with the possibility of this attack. In recent years, the cases related to DDoS attacks have increased. Although previously there has been a lot of research on DDoS attacks, cases of DDoS attacks still exist. Therefore, the research on feature selection approach has been done in effort to detect the DDoS attacks by using machine learning techniques. In this paper, to detect DDoS attacks, features have been selected from the UNSW-NB 15 dataset by using Information Gain and Data Reduction method. To classify the selected features, ANN, Naïve Bayes, and Decision Table algorithms were used to test the dataset. To evaluate the result of the experiment, the parameters of Accuracy, Precision, True Positive and False Positive evaluated the results and classed the data into attacks and normal class. Hence, the good features have been obtained based on the experiments. To ensure the selected features are good or not, the results of classification have been compared with the past research that used the same UNSW-NB 15 dataset. To conclude, the accuracy of ANN, Naïve Bayes and Decision Table classifiers has been increased by using this feature selection approach compared to the past research.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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