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Classification
Classification is the activity of assigning objects to some pre-existing classes or categories. This is distinct from the task of establishing the classes themselves (for example through cluster analysis). Examples include diagnostic tests, identifying spam emails and deciding whether to give someone a driving license. As well as 'category', synonyms or near-synonyms for 'class' include 'type', 'species', 'order', 'concept', 'taxon', 'group', 'identification' and 'division'. The meaning of the word 'classification' (and its synonyms) may take on one of several related meanings. It may encompass both classification and the creation of classes, as for example in 'the task of categorizing pages in Wikipedia'; this overall activity is listed under taxonomy. It may refer exclusively to the underlying scheme of classes (which otherwise may be called a taxonomy). Or it may refer to the label given to an object by the classifier. Classification is a part of many different kinds of activ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Taxonomy
image:Hierarchical clustering diagram.png, 280px, Generalized scheme of taxonomy Taxonomy is a practice and science concerned with classification or categorization. Typically, there are two parts to it: the development of an underlying scheme of classes (a taxonomy) and the allocation of things to the classes (classification). Originally, taxonomy referred only to the Taxonomy (biology), classification of organisms on the basis of shared characteristics. Today it also has a more general sense. It may refer to the classification of things or concepts, as well as to the principles underlying such work. Thus a taxonomy can be used to organize species, documents, videos or anything else. A taxonomy organizes taxonomic units known as "taxa" (singular "taxon"). Many are hierarchy, hierarchies. One function of a taxonomy is to help users more easily find what they are searching for. This may be effected in ways that include a library classification system and a Taxonomy for search e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Medical Classification
A medical classification is used to transform descriptions of medical diagnoses or procedures into standardized statistical code in a process known as clinical coding. Diagnosis classifications list diagnosis codes, which are used to track diseases and other health conditions, inclusive of chronic diseases such as diabetes mellitus and heart disease, and infectious diseases such as norovirus, the flu, and athlete's foot. Procedure classifications list procedure codes, which are used to capture interventional data. These diagnosis and procedure codes are used by health care providers, government health programs, private health insurance companies, workers' compensation carriers, software developers, and others for a variety of applications in medicine, public health and medical informatics, including: * statistical analysis of diseases and therapeutic actions * reimbursement (e.g., to process claims in medical billing based on diagnosis-related groups) * knowledge-based an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multiclass Classification
In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). For example, deciding on whether an image is showing a banana, peach, orange, or an apple is a multiclass classification problem, with four possible classes (banana, peach, orange, apple), while deciding on whether an image contains an apple or not is a binary classification problem (with the two possible classes being: apple, no apple). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies. Multiclass classification should not be confused with multi-label classification, where multiple labels are to be predicted for each i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification Theorem
In mathematics, a classification theorem answers the classification problem: "What are the objects of a given type, up to some equivalence?". It gives a non-redundant enumeration: each object is equivalent to exactly one class. A few issues related to classification are the following. *The equivalence problem is "given two objects, determine if they are equivalent". *A complete set of invariants, together with which invariants are realizable, solves the classification problem, and is often a step in solving it. (A combination of invariant values is realizable if there in fact exists an object whose invariants take on the specified set of values) *A (together with which invariants are realizable) solves both the classification problem and the equivalence problem. * A canonical form solves the classification problem, and is more data: it not only classifies every class, but provides a distinguished (canonical) element of each class. There exist many classification theorems in mat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Organization
Knowledge organization (KO), organization of knowledge, organization of information, or information organization is an intellectual discipline concerned with activities such as document description, indexing, and classification that serve to provide systems of representation and order for knowledge and information objects. According to ''The Organization of Information'' by Joudrey and Taylor, information organization: Issues related to knowledge sharing can be said to have been an important part of knowledge management for a long time. Knowledge sharing has received a lot of attention in research and business practice both within and outside organizations and its different levels. Sharing knowledge is not only about giving it to others, but it also includes searching, locating, and absorbing knowledge. Unawareness of the employees' work and duties tends to provoke the repetition of mistakes, the waste of resources, and duplication of the same projects. Motivating co-workers ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Binary Classification
Binary classification is the task of classifying the elements of a set into one of two groups (each called ''class''). Typical binary classification problems include: * Medical testing to determine if a patient has a certain disease or not; * Quality control in industry, deciding whether a specification has been met; * In information retrieval, deciding whether a page should be in the result set of a search or not * In administration, deciding whether someone should be issued with a driving licence or not * In cognition, deciding whether an object is food or not food. When measuring the accuracy of a binary classifier, the simplest way is to count the errors. But in the real world often one of the two classes is more important, so that the number of both of the different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a '' false positive'') is considered differently from not detecting a disease when it is present (a '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cognitive Categorization
Categorization is a type of cognition involving conceptual differentiation between characteristics of conscious experience, such as objects, events, or ideas. It involves the abstraction and differentiation of aspects of experience by sorting and distinguishing between groupings, through classification or typification on the basis of traits, features, similarities or other criteria that are universal to the group. Categorization is considered one of the most fundamental cognitive abilities, and it is studied particularly by psychology and cognitive linguistics. Categorization is sometimes considered synonymous with classification (cf., Classification synonyms). Categorization and classification allow humans to organize things, objects, and ideas that exist around them and simplify their understanding of the world. Categorization is something that humans and other organisms ''do'': "doing the right thing with the right ''kind'' of thing." The activity of categorizing things ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pattern Recognition
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent patterns. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and str ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Precision And Recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written as a formula: \text = \frac Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Written as a formula: \text = \frac Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs ( true positives), while the other three are cats ( false positives). Seven dogs were missed ( false negatives), and seven cats were correctly ex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cluster Analysis
Cluster analysis or clustering is the data analyzing technique in which task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more Similarity measure, similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistics, statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis refers to a family of algorithms and tasks rather than one specific algorithm. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small Distance function, distances between cluster members, dense areas of the data space, intervals or pa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Level Of Measurement
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. This framework of distinguishing levels of measurement originated in psychology and has since had a complex history, being adopted and extended in some disciplines and by some scholars, and criticized or rejected by others. Other classifications include those by Mosteller and Tukey, and by Chrisman. Stevens's typology Overview Stevens proposed his typology in a 1946 ''Science'' article titled "On the theory of scales of measurement". In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval", and "ratio", unifying both " qualitative" (which are described by his "nominal" ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
