Data mining methods for multiclass data: application to BI-RADS classification of breast massesDomingues, Inês
In the online FDD process, the monitoring data are classified by the trained multi-class classifier. The classifier can tell which class the data belong to. The multiclass classification-based methods can also be further classified into two subcategories, i.e. support vector machine-based and ...
Naive Bayes classification for multiclass classification expand all in page Description ClassificationNaiveBayes is a Naive Bayes classifier for multiclass learning. Trained ClassificationNaiveBayes classifiers store the training data, parameter values, data distribution, and prior probabilities. Use these clas...
In this problem, the goal is to train a classification model that predicts each class for new (test) data. “Logistic regression” is used as a multiclass classifier. Other classifiers will be considered for this problem in Section Grid search and model selection. Similar to previous problems,...
Attribute selectionClassificationMulti-relational data miningMultivalued attributesRelevance measuresAn important step in the knowledge discovery in databases (KDD) process is the attribute selection procedure, which aims at choosing a subset of attributes that can represent the important information...
multiclass classificationnaive bayesone-against-allone-against-oneOne-against-all and one-against-one are two popular methodologies for reducing multiclass classification problems into a set of binary classifications. In this paper, we are interested in the performance of both one-against-all and ...
Urszula Chajewska, Rich Caruana The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19)|August 2019 Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions whil...
classification, i.e., marriage, birthday, and traveling, etc., to anticipate products and services to facilitate the people [40]. The data about life events exist in a very small amount. Linear regression, Naïve Bayes, and nearest neighbor algorithms were evaluated on the original dataset ...
Classification techniques are becoming more popular in the fields of medical, biostatistics, bioinformatics, agriculture, business etc. as machine learning applications. Machine learning is a subfield of artificial intelligence that enables computers to understand from existing data and estimate the existence...
The SVM is a set of supervised learning methods used for classification and regression. Given a set of training examples, the SVM algorithm builds a model that predicts the class of new unseen examples. This algorithm is considered essential in both machine-learning and data-mining curriculums ...