Statistical classification

In

class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.).

Often, the individual observations are analyzed into a set of quantifiable properties, known variously as

function.

An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.

Terminology across fields is quite varied. In

.

Relation to other problems

syntactic structure

of the sentence; etc.

A common subclass of classification is probabilistic classification. Algorithms of this nature use statistical inference to find the best class for a given instance. Unlike other algorithms, which simply output a "best" class, probabilistic algorithms output a probability of the instance being a member of each of the possible classes. The best class is normally then selected as the one with the highest probability. However, such an algorithm has numerous advantages over non-probabilistic classifiers:

• It can output a confidence value associated with its choice (in general, a classifier that can do this is known as a confidence-weighted classifier).
• Correspondingly, it can abstain when its confidence of choosing any particular output is too low.
• Because of the probabilities which are generated, probabilistic classifiers can be more effectively incorporated into larger machine-learning tasks, in a way that partially or completely avoids the problem of error propagation.

Frequentist procedures

Early work on statistical classification was undertaken by

, with a new observation being assigned to the group whose centre has the lowest adjusted distance from the observation.

Bayesian procedures

Unlike frequentist procedures, Bayesian classification procedures provide a natural way of taking into account any available information about the relative sizes of the different groups within the overall population.[6] Bayesian procedures tend to be computationally expensive and, in the days before Markov chain Monte Carlo computations were developed, approximations for Bayesian clustering rules were devised.^[7]

Some Bayesian procedures involve the calculation of

: these provide a more informative outcome than a simple attribution of a single group-label to each new observation.

Binary and multiclass classification

Classification can be thought of as two separate problems – binary classification and multiclass classification. In binary classification, a better understood task, only two classes are involved, whereas multiclass classification involves assigning an object to one of several classes.^[8] Since many classification methods have been developed specifically for binary classification, multiclass classification often requires the combined use of multiple binary classifiers.

Feature vectors

Most algorithms describe an individual instance whose category is to be predicted using a

(e.g. a measurement of blood pressure). If the instance is an image, the feature values might correspond to the pixels of an image; if the instance is a piece of text, the feature values might be occurrence frequencies of different words. Some algorithms work only in terms of discrete data and require that real-valued or integer-valued data be discretized into groups (e.g. less than 5, between 5 and 10, or greater than 10).

Linear classifiers

A large number of algorithms for classification can be phrased in terms of a linear function that assigns a score to each possible category k by combining the feature vector of an instance with a vector of weights, using a dot product. The predicted category is the one with the highest score. This type of score function is known as a linear predictor function and has the following general form:

where X_i is the feature vector for instance i, β_k is the vector of weights corresponding to category k, and score(X_i, k) is the score associated with assigning instance i to category k. In discrete choice theory, where instances represent people and categories represent choices, the score is considered the utility associated with person i choosing category k.

Algorithms with this basic setup are known as linear classifiers. What distinguishes them is the procedure for determining (training) the optimal weights/coefficients and the way that the score is interpreted.

Examples of such algorithms include

• Logistic regression – Statistical model for a binary dependent variable
  • Multinomial logistic regression – Regression for more than two discrete outcomes
• Probit regression
   – Statistical regression where the dependent variable can take only two valuesPages displaying short descriptions of redirect targets
• The perceptron algorithm
• Support vector machine – Set of methods for supervised statistical learning
• Linear discriminant analysis – Method used in statistics, pattern recognition, and other fields

Algorithms

Since no single form of classification is appropriate for all data sets, a large toolkit of classification algorithms has been developed. The most commonly used include:^[9]

• Artificial neural networks
   – Computational model used in machine learning, based on connected, hierarchical functionsPages displaying short descriptions of redirect targets
• Boosting (meta-algorithm)
   – Method in machine learningPages displaying short descriptions of redirect targets
• Decision tree learning – Machine learning algorithm
  • Random forest – Binary search tree based ensemble machine learning method
• Genetic programming – Evolving computer programs with techniques analogous to natural genetic processes
  • Gene expression programming – Evolutionary algorithm
  • Multi expression programming
  • Linear genetic programming – type of genetic programming algorithmPages displaying wikidata descriptions as a fallback
• Kernel estimation
   – Window functionPages displaying short descriptions of redirect targets
  • k-nearest neighbor
     – Non-parametric classification methodPages displaying short descriptions of redirect targets
• Learning vector quantization
• Linear classifier – Statistical classification in machine learning
  • Fisher's linear discriminant
     – Method used in statistics, pattern recognition, and other fieldsPages displaying short descriptions of redirect targets
  • Logistic regression – Statistical model for a binary dependent variable
  • Naive Bayes classifier – Probabilistic classification algorithm
  • Perceptron – Algorithm for supervised learning of binary classifiers
• Quadratic classifier – used in machine learning to separate measurements of two or more classes of objectsPages displaying wikidata descriptions as a fallback
• Support vector machine – Set of methods for supervised statistical learning
  • Least squares support vector machine

Evaluation

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 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.

As a performance metric, the

in that it is not affected by the relative sizes of the different classes. ^[10] Further, it will not penalize an algorithm for simply rearranging the classes.

Application domains

Classification has many applications. In some of these, it is employed as a data mining procedure, while in others more detailed statistical modeling is undertaken.

• Biological classification
   – The science of identifying, describing, defining and naming groups of biological organisms
• Biometric
   – Metrics related to human characteristicsPages displaying short descriptions of redirect targets identification
• Computer vision – Computerized information extraction from images
  • Medical image analysis and medical imaging – Technique and process of creating visual representations of the interior of a body
  • Optical character recognition – Computer recognition of visual text
  • Video tracking – finding the item in each frame of a video sequencePages displaying wikidata descriptions as a fallback
• Credit scoring
   – Numerical expression representing a person's creditworthinessPages displaying short descriptions of redirect targets
• Document classification – Process of categorizing documents
• Drug discovery and development – Process of bringing a new pharmaceutical drug to the market
  • Toxicogenomics – branch of toxicology and genomicsPages displaying wikidata descriptions as a fallback
  • Quantitative structure-activity relationship
     – Quantitative prediction of the biological, ecotoxicological or pharmaceutical activity of a moleculePages displaying short descriptions of redirect targets
• Geostatistics – Branch of statistics focusing on spatial data sets
• Handwriting recognition – Ability of a computer to receive and interpret intelligible handwritten input
• Internet
  search engines
• Micro-array classification
• Pattern recognition – Automated recognition of patterns and regularities in data
• Recommender system – Information filtering system to predict users' preferences
• Speech recognition – Automatic conversion of spoken language into text
• Statistical natural language processing
   – Field of linguistics and computer sciencePages displaying short descriptions of redirect targets

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See also

References