Multi-class probabilistic classification using inductive and cross Venn–Abers predictors

A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification

Multinomial Naive Bayes is a supervised probabilistic classification algorithm based on Bayes theorem, designed for discrete features such as word counts. It assumes feature independence, is highly efficient, scales well to large datasets, performs strongly in text classification tasks, and delivers reliable probabilistic predictions.

We proposed a new probabilistic classification model Lock Generative Adversarial Networks (LGAN) for binary and multi-class classification problem based on Conditional Generative Adversarial Networks (GANs) to give precise prediction in real-time. By using the proposed LGAN framework, the space for searching GAN's architecture becomes considerably smaller and based on our evaluation criteria and statistical significance test, we can see the proposed network can achieve competitive results compared to previous study.

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Probabilistic Classifier in the form of a Naive Bayes Classifier. Model is trained on ctg.csv and yalefaces datasets. Useful for multi-class classification

NaiveBayes Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. It is mainly used in text classification that includes a high-dimensional training dataset. Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object. Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles. The Naïve Bayes algorithm is comprised of two words Naïve and Bayes, which can be described as: Naïve: It is called Naïve because it assumes that the occurrence of a certain feature is independent of the occurrence of other features. Such as if the fruit is identified on the bases of color, shape, and taste, then red, spherical, and sweet fruit is recognized as an apple. Hence each feature individually contributes to identify that it is an apple without depending on each other. Bayes: It is called Bayes because it depends on the principle of Bayes' Theorem Bayes' Theorem: Bayes' theorem is also known as Bayes' Rule or Bayes' law, which is used to determine the probability of a hypothesis with prior knowledge. It depends on the conditional probability. The formula for Bayes' theorem is given as: image.png Where, P(A|B) is Posterior probability: Probability of hypothesis A on the observed event B. P(B|A) is Likelihood probability: Probability of the evidence given that the probability of a hypothesis is true. P(A) is Prior Probability: Probability of hypothesis before observing the evidence. P(B) is Marginal Probability: Probability of Evidence. Advantages of Naïve Bayes Classifier: Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets. It can be used for Binary as well as Multi-class Classifications. It performs well in Multi-class predictions as compared to the other Algorithms. It is the most popular choice for text classification problems. Disadvantages of Naïve Bayes Classifier: Naive Bayes assumes that all features are independent or unrelated, so it cannot learn the relationship between features. Applications of Naïve Bayes Classifier: It is used for Credit Scoring. It is used in medical data classification. It can be used in real-time predictions because Naïve Bayes Classifier is an eager learner. It is used in Text classification such as Spam filtering and Sentiment analysis.