Statistical classification Totally Explained

Everything about Statistical Classification totally explained

Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc) and based on a training set of previously labeled items. Formally, the problem can be stated as follows: given training data

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and then use Bayes' rule to produce the class probability as in the second problem. Examples of classification algorithms include:

Linear classifiers

Quadratic classifiers

k-nearest neighbor

Boosting

Decision trees

Random forests

Neural networks

Bayesian networks

Support vector machines

Hidden Markov models An intriguing problem in pattern recognition yet to be solved is the relationship between the problem to be solved (data to be classified) and the performance of various pattern recognition algorithms (classifiers). Van der Walt and Barnard (see reference section) investigated very specific artificial data sets to determine conditions under which certain classifiers perform better and worse than others.
Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems (a phenomenon that may be explained by the No-free-lunch theorem). Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than a science.
The most widely used classifiers are the Neural Network (Multi-layer Perceptron), Support Vector Machines, k-Nearest Neighbours, Gaussian Mixture Model, Gaussian, Naive Bayes, Decision Tree and RBF classifiers.

Evaluation

The measures Precision and Recall are popular metrics used to evaluate the quality of a classification system. More recently, Receiver Operating Characteristic (ROC) curves have been used to evaluate the tradeoff between true- and false-positive rates of classification algorithms.

Application domains

Computer vision

Medical Imaging and Medical Image Analysis
Optical character recognition

Geostatistics

Speech recognition

Handwriting recognition

Biometric identification

Natural language processing

Document classification

Internet search engines

Credit scoringFurther Information

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