A Weighted Naive Bayes Algorithm Based on the Attribute Order Reduction

2013 ◽  
Vol 718-720 ◽  
pp. 2108-2112 ◽  
Author(s):  
Xi Zhou ◽  
Ke Luo
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Attribute Reduction ◽  
Order Reduction ◽  
Classification Performance ◽  
Classification Method ◽  
Bayes Classifier ◽  
Attribute Order ◽  
Bayes Algorithm

Naïve Bayes classifier was generally considered as a simple and efficient classification method. However, its classification performance was affected to some extent because of the assuming that the conditions properties were independent of each other. By analyzing the classification principle and improvement of Bayesian and the Attribute Reduction of Rough Set, this paper proposed a Naïve Bayes algorithm that the attribute order reduction and weighting were improved simultaneously. Experiment results demonstrated that the proposed method performed well in classification accuracy.

scholarly journals Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1470
Author(s):  
Guobao Zhao ◽  
Haiying Wang ◽  
Deli Jia ◽  
Quanbin Wang
Keyword(s):  
Feature Selection ◽  
Quantum Computing ◽  
Rough Set ◽  
Classification Accuracy ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Local Searches

Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to feature selection, and each of them owned the unique advantages of optimizing feature selection algorithm. Quantum computing had a good balance ability when exploring feature sets between global and local searches. Grey wolf optimizer could effectively explore all possible feature subsets, and uncertain symmetry rough set theory could accurately evaluate the correlation of potential feature subsets. QCGWORS intelligent algorithm could minimize the number of features while maximizing classification performance. In the experimental stage, k nearest neighbors (KNN) classifier and random forest (RF) classifier guided the machine learning process of the proposed algorithm, and 13 datasets were compared for testing experiments. Experimental results showed that compared with other feature selection methods, QCGWORS improved the classification accuracy on 12 datasets, among which the best accuracy was increased by 20.91%. In attribute reduction, each dataset had a benefit of the reduction effect of the minimum feature number.

scholarly journals Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Qingchao Liu ◽  
Jian Lu ◽  
Shuyan Chen ◽  
Kangjia Zhao
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classifier Ensemble ◽  
Optimal Threshold ◽  
Incident Detection ◽  
Bayes Classifier ◽  
Traffic Incident ◽  
Better Than

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Analisa Topik Pendidikan Dalam Al-Quran dengan Pendekatan Text Mining

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bustami Yusuf ◽  
Muhammad Zaeki ◽  
Hendri Ahmadian ◽  
Khairan Ar ◽  
Sri Wahyuni
Keyword(s):  
Evaluation Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Sources Of Knowledge ◽  
Scientific Disciplines ◽  
Study Results ◽  
The World ◽  
The Subject ◽  
Bayes Algorithm

Education is one of the sciences that makes humans much better by learning various scientific disciplines. Al-Quran is one of the sources of knowledge that is believed by Muslims around the world. Because technology has penetrated almost every domain of our lives , including the world of education. Thus, the authors make technology as tool  for researching educational topics in Al-Quran by implementing text exploration .The research was carried out by making some basic words that were related to the subject of education as the keywords in this study. The keywords are “Ajar”, “Bicara”, “Cipta”, “Dengar”, “Ingat” and “Lihat”. Then, the authors implemented the Naïve Bayes Classifier algorithm. To test and evaluate the results, the author used two methods, i.e. recall and precision. The study results are the keyword “cipta” by 3.05 %, “Ingat” 2.25 %, “Ajar” 1.96 %,“Lihat” 0.82 %, finally “Dengar” 0.62% and “Bicara” 0.34% with  total  weight of 3,516 words that  have been filtered. The overall percentage of the results is 9.04% of the total number of words 38,761 in the Al-Quran. For the Naïve Bayes algorithm evaluation method,  the recall and precision scores are 0.605 and 0.366, respectively.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

A Heuristic Attribute Reduction Based on Multi-Granularity Rough Set

2014 ◽  
Vol 513-517 ◽  
pp. 973-977
Author(s):  
Zhi Li Pei ◽  
Jian Hong Qi ◽  
Li Sha Liu ◽  
Qing Hu Wang ◽  
Ming Yang Jiang ◽  
...  
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Attribute Reduction ◽  
Experimental Results ◽  
Attribute Importance

In 2012, Wang Zuofei built up granularity-function and applied it to the measure of attribute importance and attribute reduction. On this basis, granularity-function based upon pessimistic and optimistic multi-granularity rough set is constructed. It is applied to the calculation of attribute importance and attribute reduction. According to the experimental results, the method can reduce the dimension of features and obviously improve the classification accuracy and efficiency.

scholarly journals Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer

2018 ◽  
Vol 5 (3) ◽  
pp. 269
Author(s):  
Yoga Dwitya Pramudita ◽  
Sigit Susanto Putro ◽  
Nurul Makhmud
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Method ◽  
Easy Access ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
News Stories ◽  
Long Time ◽  
Sports News

<p>Dokumen berita olahraga dalam bentuk web kini memiliki jumlah yang besar dalam kurun waktu singkat. Untuk kemudahan akses dokumen perlu melakukan pengelompokan dokumen berita kedalam beberapa kategori. Hal tersebut bertujuan agar berita olahraga tersusun sesuai dengan kategori yang ditentukan. Berita dapat dikelompokkan secara manual oleh manusia, akan tetapi hal tersebut membutuhkan waktu yang lama untuk melakukan kategorisasi. Metode klasifikasi diusulkan dalam penelitian ini untuk melakukan pengkategorian secara otomatis dokumen berita. Tujuan dilakukannya klasifikasi adalah untuk mempercepat dan mempermudah dalam pemberian kategori, sehingga dapat meningkatkan efisiensi waktu. Pada penelitian ini menggunakan metode klasifikasi Naïve Bayes Classifier. Sebelum dilakukan klasifikasi ada proses preprocessing dengan menggunakan Enhanced Confix Striping Stemmer.  Hal ini bertujuan untuk mengembalikan ke bentuk kata dasar, sehingga data berkurang dan proses komputasi menjadi lebih efisien. Pengujian dilakukan menggunakan 18 berita olahraga yang dipilih secara acak oleh user atau tester, dari 18 berita yang diujikan terdapat 14 berita yang bernilai benar atau relevan dengan analisis yang dilakukan use atau tester pada berita uji. Dari penelitian ini dapat disimpulkan bahwa Aplikasi Klasifikasi Berita Olahraga menggunakan Metode Naïve Bayes dengan Enhanced Confix Striping Stemmer mampu mengklasifikasi berita olahraga sesuai dengan kategori masing-masing, seperti Sepak Bola, Basket, Raket, Formula 1, Moto GP dan olahraga lainnya dengan keakuratan sebesar 77%.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"> </p><p>Web-based sports news currently has a considerable amount of documents. News documents need to be grouped into multiple categories for easy access. The goal is that sports news is structured according to the specified category. News can be grouped manually by humans, but it takes a long time to categorize if it involves large documents. Classification method is proposed in this research to categorize automatically news document. The purpose of doing the classification is to accelerate and simplify the granting of categories, thereby increasing the efficiency of time. In this research using the Naïve Bayes Classifier classification method. Prior to classification there is a preprocessing process using Enhanced Confix Striping Stemmer. It aims to return to the basic word form, so the data is reduced and the computing process becomes more efficient. From the test using 18 sports news randomly selected by the user or tester, there are 14 news stories that are true or relevant to the analysis by the user or the tester on the test news. This study concludes that the Sports News Classification Application using the Naïve Bayes Method with Enhanced Confix Striping Stemmer is able to classify sports news according to their respective categories, such as Football, Basket, Racquet, Formula 1, Moto GP and other sports with accuracy of 77%.</p>

scholarly journals Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes

2019 ◽  
Vol 9 (2) ◽  
pp. 97
Author(s):  
Firman Tempola
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Attribute Weights ◽  
The Status ◽  
Bayes Algorithm

<p class="JGI-AbstractIsi">This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.</p>

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