Naïve Bayes vs. Support Vector Machine: Resilience to Missing Data

Shi, Hongbo; Liu, Yaqin

doi:10.1007/978-3-642-23887-1_86

Cited by 18 publications

(11 citation statements)

References 5 publications

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“…The Naïve Bayes classification method is adopted in this study primarily for its ability to handle missing features, which occurs for some of the neuropsychological assessments (Liu et al, 2005 ; Shi and Liu, 2011 ). A Naïve Bayes classifier is a simple probabilistic classifier based on the application of Bayes' theorem (described mathematically below) with the assumption of probabilistic independence between every pair of features; in practice this is rarely true, as certain features can be correlated, but Naïve Bayes classifiers demonstrate remarkably robust performance on features which are not strictly independent (Zhang, 2004 ).…”

Section: Methodsmentioning

confidence: 99%

A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Wang

Redmond

Bertoux

et al. 2016

Front. Aging Neurosci.

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The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration.

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Section: Methodsmentioning

confidence: 99%

A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Wang

Redmond

Bertoux

et al. 2016

Front. Aging Neurosci.

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“…Among the classification algorithms, there is a range of classifiers. For instance, the Naïve Bayes classifier is a typical generative classifier and is regarded as a special case of Bayesian Network classifiers [8]. The support vector machine (SVM) algorithm learns how important each training data point is to distinguish the decision boundaries between the two classes during learning.…”

Section: Use Of Machine Learning and Deep Learning For Biomedical Litmentioning

confidence: 99%

“…There are three important types of neural networks used in deep learning models: convolutional neural networks (CNN), recurrent neural networks (RNN), and multi-layer artificial neural networks (ANN). Deep convolutional networks have made breakthroughs in image, video, voice, and audio processing, while recurrent networks have shed light on sequential information such as text and voice [8]. In contrast, multi-layer ANN is a suitable option for the classification of textual data structured in a tabular form.…”

Section: Use Of Machine Learning and Deep Learning For Biomedical Litmentioning

confidence: 99%

Deep Learning Based Biomedical Literature Classification Using Criteria of Scientific Rigor

et al. 2020

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A major blockade to support the evidence-based clinical decision-making is accurately and efficiently recognizing appropriate and scientifically rigorous studies in the biomedical literature. We trained a multi-layer perceptron (MLP) model on a dataset with two textual features, title and abstract. The dataset consisting of 7958 PubMed citations classified in two classes: scientific rigor and non-rigor, is used to train the proposed model. We compare our model with other promising machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosted Tree (GBT) approaches. Based on the higher cumulative score, deep learning was chosen and was tested on test datasets obtained by running a set of domain-specific queries. On the training dataset, the proposed deep learning model obtained significantly higher accuracy and AUC of 97.3% and 0.993, respectively, than the competitors, but was slightly lower in the recall of 95.1% as compared to GBT. The trained model sustained the performance of testing datasets. Unlike previous approaches, the proposed model does not require a human expert to create fresh annotated data; instead, we used studies cited in Cochrane reviews as a surrogate for quality studies in a clinical topic. We learn that deep learning methods are beneficial to use for biomedical literature classification. Not only do such methods minimize the workload in feature engineering, but they also show better performance on large and noisy data.

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“…It is true that several probability equations, including Decision Tree, Neutral Networks, Support Vector Machine (SVM), can be applied to analyze consumer behavior, the best result in behavior analysis [9], however, is carried out by Naï ve Bayesian Classifications [10]. In addition, Naï ve Bayesian Classifications continues to operate to bring about the result even in case of partly lost data making this more efficient that Support Vector Machine (SVM) [11].…”

Section: A Machine Learningmentioning

confidence: 99%

Application of Artificial Intelligent in the Prediction of Consumer Behavior from Facebook Posts Analysis

Kachamas¹,

Akkaradamrongrat²,

Sinthupinyo³

et al. 2019

IJMLC

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The undeniable fact is that online business today has increased at a very fast pace everywhere around the globe. This happens through the widely used of Social Media, especially Facebook which is the most popular platform in the world. It would be really useful for the digital marketers, if there is a certain tool that can predict the intentions of the web patrons when the brand is posting the message to communicate with their fans or followers.The aim of this research is to develop an analytic tool which can support online vendors to predict behaviors of the patrons according to Dentsu's AISAS perspective. An Artificial intelligent model was developed by the results from 75 specialists who evaluated the behavior that will likely occur after the comments have been posted. The results, hence, were collected and prepared for the data modelling process using the Naï ve Bayes probability concept, afterwards, testing for the model's accuracy with 10-fold cross validation technique. As the previous study indicated, Naï ve Bayes technique gives the best result for the behavior analysis, which is also true with this study. The predictive model for AISAS behavior from this study can give average accuracy higher than 86 percent.When bringing the AISAS Model to test with 30 live users who are online vendors, we can conclude that the overall results of model have been greatly appreciated and effectively satisfied. Most vendors also agreed on the ease of use, which creates high chances of business opportunities.

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Naïve Bayes vs. Support Vector Machine: Resilience to Missing Data

Cited by 18 publications

References 5 publications

A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Deep Learning Based Biomedical Literature Classification Using Criteria of Scientific Rigor

Application of Artificial Intelligent in the Prediction of Consumer Behavior from Facebook Posts Analysis

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