Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy

Abellán, Joaquín; Castellano, Javier G.

doi:10.3390/e19060247

Cited by 34 publications

(19 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MCCV is based on multiple executions of the train and test method, with a defined random percentage, split into training and test parts. In search of an effective, simple classification method, we implemented the Naive Bayes classifier [14], the weighted classification 811 method [14], and the local kNN using Manhattan, Euclidean, and Canberra metrics [14]. Naive Bayes uses sum instead of multiplication to avoid zeroing of parameters and descriptors' indiscernibility ratio.…”

Section: Searching For the Dedicated Method-selected Resultsmentioning

confidence: 99%

“…There are many methods of analysis in the field of using the electronic nose in beekeeping, including linear discriminant analysis (LDA), principal component analysis (PCA), and cluster analysis (CA) with the furthest neighbor method (kNN). Good results have also been obtained using the artificial neural network (ANN) machine learning techniques, which use a neural network model based on a multilayer perceptron that learned using a backpropagation algorithm [8][9][10][11][12][13][14][15].…”

Section: Achievements To Date In the Use Of Gas Sensors For This Typementioning

confidence: 99%

See 1 more Smart Citation

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Wilk

Bąk

Artiemjew

et al. 2020

Sensors

View full text Add to dashboard Cite

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

show abstract

Section: Searching For the Dedicated Method-selected Resultsmentioning

confidence: 99%

Section: Achievements To Date In the Use Of Gas Sensors For This Typementioning

confidence: 99%

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Wilk

Bąk

Artiemjew

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Metode classifier juga dapat digunakan pada algoritma Naive Bayes, dimana metode tersebut akan mampu melihat peluang dari setiap kondisi [7]. Algoritma Naive Bayes dalam pengklasifikasian dokumen akan dapat mengklasifikasikan dengan melihat probabilitas pada setiap model [8]. Pada penerapan analisis sentimen terhadap suatu produk di Twitter menggunakan Naive Bayes dan Information Gain mampu menghasilkan tingkat akurasi tertinggi senilai 85% dan titik terendah pengujian menghasilkan tingkat akurasi 50% [9].…”

Section: Pendahuluanunclassified

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

Anggita

Ikmah

2020

RESTI

View full text Add to dashboard Cite

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

show abstract

“…The experiment is tested using UCI datasets and results are compared with SBC, NB, C4.5, and ABC. [17]A new variable selection IIG proposed to use with NB to enhance the performance prediction. The IIG method is proposed to eliminate the drawbacks of IG (non-negative & Threshold value).…”

Section: Related Workmentioning

confidence: 99%

Customer Behavior Analysis using Weighted Selective Naive Bayesian

subramanian*,

PRABHA

2020

IJITEE

View full text Add to dashboard Cite

The predominant success of each enterprise relies upon the efficient analysis of customer behavior and a deep understanding of customers' needs. Performing better customer analysis provides an effective analysis of potential customers, better decision-making, improved business processes, the measure of customers churn and enhance customer retention. Efficient customer analysis can be performed using the Naive Bayes (NB), which is a simple classifier used for predicting customer behavior. However, the performance prediction of Naive Bayes is strongly affected in some real-time datasets which involve the presence of correlated attributes and this creates a breach of the assumption made by the Naive Bayes model on the dataset. To enhance the performance prediction of the NB and to eliminate correlated variables, this research suggests a simple WSNB (weighted Selective Naive Bayesian) method that uses the C4.5 DT for selecting the attributes with high information values. Then the selected weighted attributes are further used with the Naive Bayes for improving performance prediction. The Experimental approach is tested with a bank client dataset, which indicates that WSNB makes better predictions than the standard Naive Bayes. Also, WSNB reduces the running time of the classifier by eliminating the correlated attributes, which in effect minimize the size of learning and testing data

show abstract

Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy

Cited by 34 publications

References 26 publications

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

Customer Behavior Analysis using Weighted Selective Naive Bayesian

Contact Info

Product

Resources

About