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Introduction
Published in Chandrasekar Vuppalapati, Democratization of Artificial Intelligence for the Future of Humanity, 2021
In supervised learning, an algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output [10]. That is, the humans label desired class or output so that the machine identifies a pattern between a given input and a labeled output. The labeled output is dependent-variable, i.e., depends upon the values of attributes, independent variables, in a given data set. For example, take USA Housing46 data set (please see Table 1: Housing Data). The dataset contains independent variables: Average Area Income, Average Area House Age, Average Area Number of Rooms, Average Area Number of Bedrooms, Area Population, and Address. The class variable or dependent variable Price. In other words, the purpose of the model construction is to identify and predict Price of the house based on independent variable.
Machine Learning
Published in Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza, Industrial Applications of Machine Learning, 2019
Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza
The parameters to be estimated are the a priori probabilities p(c) and the mean μc,i $ \mu _{c,i} $ and standard deviation σc,i $ \sigma _{c,i} $ of each predictor variable Xi $ X_i $ for each c value of the class variable. Maximum likelihood is usually employed for estimations.
Enchancing Medical Problem Solving through the Integration of Temporal Abstractions with Bayesian Networks in Time-Oriented Clinical Domains
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
Kalia Orphanou, Athena Stassopoulou, Elpida Keravnou
Naïve Bayes classifiers belong to the family of probabilistic graphical models based on Bayes theorem with the “naïve” assumption that the effect of an attribute value on a given class is independent of the values of the other attributes [96,97]. This assumption is called class conditional independence. In our study, the class variable is the Disease (CHD event), and all the TAR attributes are connected to the class variable as shown in Figure 25.11.4 The Disease is a binary node taking the values (a) 0: absence of CHD event and (b) 1: presence of CHD event. The goal is to classify the presence or absence of a CHD event given the pattern of occurrence of each TAR in the relevant history of the case in question.
Selective AnDE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI)
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Shenglei Chen, Xin Ma, Linyuan Liu, Limin Wang
Given a data set , let represent attributes. A particular instance is expressed as , where is the specific value taken by attribute . The class variable is denoted by , and the value is denoted by . The basic task of a classification problem is to estimate the conditional probability distribution over all the possible classes for a new example , and then predict the class of as a that maximises .
Identification of healthy biological leafs using hybrid-feature classifier
Published in The Imaging Science Journal, 2021
Vijaya Patnaik, Monalisa Mohanty, Asit Kumar Subudhi
The type of classifier [44] is utilized to train a model which produces a given matrix got from the datasets of positive and negative training. The NB classification principle is utilized to compute the posterior probability through the prior probability of an image by making use of the theorem Bayes’. Here, 10 various types of leaves are utilized for training and testing the dataset. The NB utilized causal relations between the kind of patch and the comparing matrix of features to produce a classification system. Then at this point, this system was utilized to make predictions. The model with the class variable as ‘C’ can be expressed by using Bayes Theorem as where j is represented as the number of the class, Y is represented as the extracted multi-dimensional vector of features.
Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus
Published in Applied Artificial Intelligence, 2019
Abir Alharbi, Munirah Alghahtani
The Saudi type 2-diabetes Dataset was obtained from King Khalid University Hospital. There are 110 (55%) cases in class (1) and 90 (45%) cases in class (0), Where (1) means a positive test for diabetes and (0) is a negative test for diabetes. Diabetes Attribute information is given below: Number of times pregnant.Glucose tolerance test Fasting Plasma Glucose (FBS).Glucose tolerance test at 1 hours (1H).Glucose tolerance test at 2 hours (2H).Diastolic blood pressure (mm Hg).Hemoglobin A1C HBA1C.Body mass index (weight in kg/height in m).Age (years).Class variable (0 or 1).