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Associative Classifier
An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute". Model The model generated by an AC and used to label new records consists of association rules Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.P ..., where the consequent corresponds to the class label. As such, they can also be seen as a list of "if-then" clauses: if the record matches some criteria (expressed in the left side of the rule, also called antecedent), it is then labeled accordingly to the class on the right side of the rule (or consequent). Most ACs read ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs. It infers a function from ' consisting of a set of ''training examples''. In supervised learning, each example is a ''pair'' consisting of an input object (typically a vector) and a desired output value (also called the ''supervisory signal''). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Association Rules
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.Piatetsky-Shapiro, Gregory (1991), ''Discovery, analysis, and presentation of strong rules'', in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., ''Knowledge Discovery in Databases'', AAAI/MIT Press, Cambridge, MA. In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule \ \Rightarrow \ found in the sales data of a supermarket would indicate that if a customer buy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bing Liu (computer Scientist)
Bing Liu (born 1963) is a Chinese-American professor of computer science who specializes in data mining, machine learning, and natural language processing. In 2002, he became a scholar at University of Illinois at Chicago. He holds a PhD from the University of Edinburgh (1988). His PhD advisors were Austin Tate and Kenneth Williamson Currie, and his PhD thesis was titled ''Reinforcement Planning for Resource Allocation and Constraint Satisfaction''. Academic research He developed a mathematical model that can reveal fake advertising. Also, he teaches the course "Data Mining" during the Fall and Spring semesters at UIC. The course usually involves a project and various quiz/examinations as grading criteria. He is best known for his research on sentiment analysis (also called opinion mining), fake/deceptive opinion detection, and using association rules for prediction. He also made important contributions to learning from positive and unlabeled examples (or PU learning), Web data ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
