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An Integration of Blockchain and Machine Learning into the Health Care System
Published in Om Prakash Jena, Sabyasachi Pramanik, Ahmed A. Elngar, Machine Learning Adoption in Blockchain-Based Intelligent Manufacturing, 2022
Mahita Sri Arza, Sandeep Kumar Panda
Consider the same example as in the logistic regression section, but now the Naïve Bayes classifier is employed to predict whether a person would buy a car based on their age and salary. Figure 3.5 shows the visualization of the training data set, and Figure 3.6 shows the visualization of the test data set of the Naïve Bayes classifier. Figure 3.5 demonstrates that Naïve Bayes has segregated the data points with a fine boundary, resulting in a Gaussian curve. Figure 3.6 shows the final output for the test set data. The classifier puts to use a Gaussian curve to distinguish between the variables “purchased” and “not purchased,” as displayed, as well as a few incorrect predictions. Other applications of the Naïve Bayes classifier include text classification, spam filtering, credit scoring, and the like.
Supervised Learning
Published in Peter Wlodarczak, Machine Learning and its Applications, 2019
One of the simplest Bayesian models and one of the simplest machine learning methods is the naïve Bayes classifier. It is a generative probabilistic model and is often a good starting point for an machine learning project since it can be extremely fast in comparison to other classifiers, even on large data sets, and it is simple to implement. In practice, the na¨ıve Bayes classifier is widely used because of its ease of use and despite its simplicity it has proven to be effective for many machine learning problems. For instance, spam filters often use Bayesian spam filtering to calculate the probability that an email is spam or legitimate. It uses word frequencies, a bag of words, as input to detect spam mails and has proved to be an effective filtering method. The na¨ıve Bayes classifier introduces the assumption that all variables vj are conditionally independent. This is a na¨ıve assumption, hence the name, since variables often depend on each other. This assumption simplifies equation 4.7 to: () P(c|x)=P(c)Πj=1nP(vj|c)P(x)
Sensor- and Recognition-Based Input for Interaction
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
The naïve Bayes classifier assumes that the value of a given feature is independent of all the others. This property of conditional independence may not actually apply to the data set, but its assumption simplifies computation and often may not matter in practice (hence the label “naïve”). Assuming observations of the form x 5 ^x1, x2, ..., xn&, the posterior probability of a class C is P(Cfx) 5 P(C) P(xfC) by the Bayes rule. Naïve Bayes treats each feature as independent: P(Cfx) 5 P(C)PiP(xifC). Because each feature is modeled independently, naïve Bayes is particularly suited to high-dimensional feature spaces and large data sets. Each feature can be continuous or discrete. Discrete variables are often modeled as a histogram (or probability mass function), while continuous variables can be quantized or binned to discrete values, or modeled as a Gaussian or other parametric distribution.
Efficient Machine Learning-based Approach for Brain Tumor Detection Using the CAD System
Published in IETE Journal of Research, 2023
Mohamed Amine Guerroudji, Zineb Hadjadj, Mohamed Lichouri, Kahina Amara, Nadia Zenati
Classification is the last step in a computer-aided diagnosis (CAD) system. The CAD system has three main steps: segmentation, description, and classification. Segmentation step detects the region of interest in an image, description step determines the characteristics of the segmented region of interest, and classification step exploits the description result to be able to decide on the pathological nature of the region of interest (tumor). The classifier used in this system is the Bayes classifier, which is a probabilistic machine-learning algorithm. The training of this classifier was carried out using 36 images, which is two-thirds of the database. The testing was carried out using the remaining one-third of the database. In Figure 11, the frequencies of the different attributes (characteristics) of the tumors were calculated and plotted as normal probability graphs. The purpose of these plots is to determine if the data is normally distributed. A normal distribution is a type of statistical distribution where most of the data is centered around the mean and the data becomes less frequent as you move away from the mean. If the data is normally distributed, the plot will be linear, as demonstrated in Figure 11. The paragraph states that all normal probability graphs for the nine attributes were found to be linear, indicating that the data is normally distributed.
Classification of Customer Reviews Using Machine Learning Algorithms
Published in Applied Artificial Intelligence, 2021
NB Tree is a supervised classifier that combines Bayesian rule and decision tree. This algorithm uses the Bayes rule to calculate the likelihood of each given class of instances, assuming that the properties are the conditional independent of the label given (Gulsoy and Kulluk 2019). A Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions. In simple terms, a Naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class (i.e. attribute) is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4 inches in diameter. Even if these features depend on each other or upon the existence of the other features, a Naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.
Advanced Classification of Architectural Heritage: A Corpus of Desert on Rout Caravanserais
Published in International Journal of Architectural Heritage, 2020
Elham Andaroodi, Frederic Andres
The research implemented Bayesian Networks in a different way. It is used to model uncertain descriptive design features. Here, the classification algorithm based on Naïve Bayes inference is used. It worked well with the data set of caravanserais, combining categorical or qualitative textual features. Naïve Bayes is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature (Ray 2018). For example, a caravanserai belongs to the selected corpus of this research, if it has open courtyard, the ring of rooms are separated from stables, and is located outside historic cities. Even if these features depend on each other or upon the existence of the other features, all of these properties independently, contribute to the probability that this caravanserai belongs to this corpus and that is why it is known as “Naive.” Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. If the data can be converted into a frequency table, then a table of likelihood can be created, and by using Naïve Bayesian equation, it is possible to calculate the posterior probability for each class (Ray 2018).