GM-RKB: Supervised Classification Task

AKA: Supervised Classification, Supervised Classification Function Learning Task, Supervised Classification Learning, Classification Function Learning Task, Inductive Classification Task, Classification Function Learning Task, Probabilistic Classification Function Learning Algorithm.
Context:
- Input:
  - a Training Dataset where the Target Attribute is a Discrete Attribute.
  - a Testing Dataset
- Output: a predicted Class Value for each Testing Record.
  - Optional Output: a Learned Classification Function.
- It can be:
  - a Fully-Supervised Classification Task.
  - a Semi-Supervised Classification Task.
- It can be:
  - a Supervised Binary Classification Task.
  - a Supervised N-ary Classification Task.
- It can be:
  - a Univariate Supervised Classification Task.
  - a Multivariate Supervised Classification Task.
- It can be solved by a Supervised Classification Algorithm and implemented into a Supervised Classification System.
- It is a Simple Output Learning Task.
Example(s):
- a Classification Rule Learning Task.
- a Classifying Neural Network Learning Task.
Counter-Example(s):
- a Supervised Regression Task.
See: Probabilistic Classification Task.

References

(Witten & Frank, 2000) => Ian H. Witten, and Eibe Frank. (2000). "Data Mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann.
- Four basically different styles of learning appear in data mining applications. In classification learning, a learning scheme takes a set of classified examples from which it is expected to learn a way of classifying unseen examples...
- Classification learning is sometimes called supervised because, in a sense, the scheme operates under supervision by being provided with actual outcome for each of the training examples ...

(Domingos, 1996) => Pedro Domingos. (1996). "Unifying Instance-Based and Rule-Based Induction." In: Machine Learning, 24(2). [doi>10.1023/A:1018006431188]
- Inductive learning is the explicit or implicit creation of general concept or class descriptions from examples. Many induction problems It can be described as follows. A training set of preclassified examples is given, where each example (also called observation or case) is described by a vector of features or attribute values, and the goal is to form a description that can be used to classify previously unseen examples with high accuracy.

(Kohavi, 1995) => Ron Kohavi. (1995). "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection." In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995).
- A classifier is a function that maps an unlabelled instance to a label using internal data structures. An inducer or an induction algorithm builds a classifier from a given dataset. CART and C 4 5 (Brennan, Friedman Olshen &. Stone 1984, Quinlan 1993) are decision tree inducers that build decision tree classifiers. In this paper we are not interested in the specific method for inducing classifiers, but assume access to a dataset and an inducer of interest.