In the field of machine learning and statistics, classification methods are methods and criteria for classifying objects or situations. Such a method is also referred to as a classifier. Many methods can be implemented as an algorithm; it is also referred to as machine or automatic classification. Classification methods are always application-related, so many different methods exist.
The algorithm which implements classification is known as a classifier. In the narrow sense, the classification methods are used for classifying objects into existing classes. Classification methods play a role in pattern recognition, artificial intelligence, documentation science and information retrieval, among others. Different parameters can be determined for the assessment of a classifier.
Bayesian classification provides a way of taking into account any available information about the relative sizes of the different groups. Bayesian procedures tend to be computationally expensive. A large number of algorithms for classification can be phrased in terms of a linear function which assigns a score to each possible category. A linear classifier achieves making a classification decision based on the value of a linear combination of the characteristics.
Types of classification methods
Since a strictly hierarchical classification of classification methods is hardly possible, they can best be classified on the basis of various characteristics:
- Manual and automatic procedures
- Numerical and non-numeric methods
- Statistical and distribution-free procedures
- Monitored and unmonitored procedures
- Fixed and learning methods
- Parametric and non-parametric methods
In the case of automatic procedures, the classification takes place using an automatic process by software. The process of machine classification can be described as a formal method of decision-making in new situations based on learned structures. Machine classification is a sub-area of machine learning.
More precisely, this is the creation of an algorithm (the learning algorithm) that calculates structures, applied to known and already classified cases (the data base). These newly learned structures allow another algorithm (the evaluating algorithm) to assign a new and previously unknown case to one of the known target classes based on the observed attributes and their characteristics.
Statistical methods are based on density calculations and probabilities, while distribution-free methods use clear partitions to separate classes. The boundaries between the individual classes in the characteristic space can be specified by a discriminant function.
Examples of statistical methods are the Bayes classifier, the fuzzy pattern classifier and the core density estimator. The calculation of separation surfaces is possible by so-called support vector machines. Creating structures from existing data is also known as pattern recognition, discrimination, or supervised learning. Class divisions are given, which can also be done by sampling. In contrast, there is unmonitored learning in which the classes of the data are not predetermined, but also must be learned. However, in the case of emboldening learning, information can be added as to whether a class classification was right or wrong. An example of unsupervised procedures is cluster analysis. Parametric methods are based on parametric probability densities, while nonparametric methods (e.g. nearest neighbor classification) are based on local density calculations.
What is Statistical Classification in Short?
Statistical classification in supervised learning trains to categorize based upon the relevance to known data.
The algorithms (classifiers) sort the unlabeled data to categories of information. A good example is spam filter classifying the emails as either “spam” or “not-spam”. In the unsupervised learning, the training dataset are not labeled hence the system get the chance to create own rules of classification and find out hidden patterns.