High Throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor

2021 ◽  
Author(s):  
Ashin Antony ◽  
Devi A ◽  
Kuruvilla Varghese
Keyword(s):  
High Throughput ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tree Algorithm ◽  
Hoeffding Tree

scholarly journals Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms

2018 ◽  
Vol 7 (1.7) ◽  
pp. 137 ◽  
Author(s):  
Danda Shashank Reddy ◽  
Chinta Naga Harshitha ◽  
Carmel Mary Belinda
Keyword(s):  
Brain Tumor ◽  
Decision Tree ◽  
Naive Bayes ◽  
Classification Problem ◽  
Naïve Bayes ◽  
World Health ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Computer Tomography Scan ◽  
Bayes Algorithm

Now a day’s many advanced techniques are proposed in diagnosing the tumor in brain like magnetic resonance imaging, computer tomography scan, angiogram, spinal tap and biospy. Based on diagnosis it is easy to predict treatment. All of the types of brain tumor are officially reclassified by the World Health Organization. Brain tumors are of 120 types, almost each tumor is having same symptoms and it is difficult to predict treatment. For this regard we are proposing more accurate and efficient algorithm in predicting the type of brain tumor is Naïve Bayes’ classification and decision tree algorithm. The main focus is on solving tumor classification problem using these algorithms. Here the main goal is to show that the prediction through the decision tree algorithm is simple and easy than the Naïve Bayes’ algorithm.

scholarly journals Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

2021 ◽  
Vol 6 (3) ◽  
pp. 178-188
Author(s):  
Adhitya Prayoga Permana ◽  
Kurniyatul Ainiyah ◽  
Khadijah Fahmi Hayati Holle
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Test Results ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Start Up ◽  
The Many ◽  
Start Ups

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

scholarly journals Efficient Detection and Classification of Diabetic Foot Ulcer Tissue using PSO Technique

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1006 ◽  
Author(s):  
Babu K S ◽  
Sukanta Sabut ◽  
Nithya D K
Keyword(s):  
Diabetes Mellitus ◽  
Naive Bayes ◽  
Optimization Technique ◽  
Foot Ulcer ◽  
Naïve Bayes ◽  
Optimization Techniques ◽  
Diabetic Patients ◽  
Diabetic Wound ◽  
Efficient Detection ◽  
Hoeffding Tree

Assessment of a diabetic wound is very much important to determine the healing status. Foot ulcer is most commonly observed problem of diabetic patients. A diabetic wound is observed for approximately 15 per cent of diabetic patients. Diabetic wound is a major concern of diabetes mellitus. The foot ulcer is the very much harm full problem related to diabetes mellitus. Here particle swarm optimization (PSO) based optimization technique is used for segmentation of diabetic wounds and classifying into three types of tissues i.e. granulation, necrotic and slough. After the segmentation the different textural features are extracted through Gray Level Co-occurrence Matrix (GLCM). All these features were then fed to two different classifiers, Naive bayes and Hoeffding tree for classifying the tissue types. The experimental results showed that the classification accuracy, sensitivity, specificity are 90.90%, 100%, 87.5% by Naive bayes, and 81.81%, 100%, 77.7% by Hoeffding tree respectively. Hence the PSO optimization techniques along with Naive bayes classifier could be used for the effective segmentation of diabetic wound images.  

scholarly journals ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 88 ◽  
Author(s):  
Sumpena Sumpena ◽  
Yuma Akbar ◽  
Nirat Nirat ◽  
Mario Hengky
Keyword(s):  
Intensive Care ◽  
Decision Tree ◽  
Medical Information ◽  
Naive Bayes ◽  
Venous Pressure ◽  
Naïve Bayes ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Sepsis Diagnosis ◽  
Bayes Algorithm

Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to determine the level of accuracy of patient care and supervision information in the ICU. The results showed that the decision tree algorithm C.45 has an accuracy of 81.55% and Naive Bayes of 81.54%. The decision tree C.45 algorithm has almost the same advantages as the Naive Bayes algorithm.

scholarly journals To Identify the Improvement Pattern of Self-financing Arts and Science College Student’s Academic Performance using Classification Algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 2429-2433
Keyword(s):  
Academic Performance ◽  
Decision Tree ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Science College ◽  
Bayes Algorithm

The aim of this research work is to identify the improvement pattern of academic performance of final year students of self-financing arts and science colleges. The data was collected from the students of nine Arts and Science Colleges. The data contains demographic, socio-economic, residence and college location, subjects, infrastructural facilities, faculty concern and self-motivation attributes. The classification algorithms like Naïve Bayes, Decision tree and CBPANN are applied on the student’s data. The outcome of the research can be used to improve the academic performance students studying in self-financing arts and science colleges located in educationally backward areas. The experiment results shows that the accuracy value for Naïve Bayes algorithm is 92.63%, accuracy value for Decision Tree algorithm is 96.41% and accuracy value for CBPANN algorithm is 99.49%

scholarly journals Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1084-1096
Author(s):  
T. Dinesh
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Decision Tree Algorithm ◽  
Fake News ◽  
Tree Algorithm ◽  
Political News ◽  
Machine Learning Classifiers ◽  
Bayes Algorithm

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.

scholarly journals Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier

2004 ◽  
Vol 9 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Meir Glick ◽  
Anthony E. Klon ◽  
Pierre Acklin ◽  
John W. Davies
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Naive Bayes ◽  
Computing Time ◽  
Poor Quality ◽  
Naïve Bayes ◽  
Classification Model ◽  
Bayes Classifier ◽  
Short Computing Time

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier. ( Journal of Biomolecular Screening 2004:32-36)

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