|
Decision-theoretic Rough Sets
In the mathematical theory of decisions, decision-theoretic rough sets (DTRS) is a probabilistic extension of rough set classification. First created in 1990 by Dr. Yiyu Yao, the extension makes use of loss functions to derive \textstyle \alpha and \textstyle \beta region parameters. Like rough sets, the lower and upper approximations of a set are used. Definitions The following contains the basic principles of decision-theoretic rough sets. Conditional risk Using the Bayesian decision procedure, the decision-theoretic rough set (DTRS) approach allows for minimum-risk decision making based on observed evidence. Let \textstyle A=\ be a finite set of \textstyle m possible actions and let \textstyle \Omega=\ be a finite set of s states. \textstyle P(w_j\mid is calculated as the conditional probability of an object \textstyle x being in state \textstyle w_j given the object description \textstyle /math>. \textstyle \lambda(a_i\mid w_j) denotes the loss, or cost, for performing ac ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Theory
Decision theory or the theory of rational choice is a branch of probability theory, probability, economics, and analytic philosophy that uses expected utility and probabilities, probability to model how individuals would behave Rationality, rationally under uncertainty. It differs from the Cognitive science, cognitive and Behavioural sciences, behavioral sciences in that it is mainly Prescriptive economics, prescriptive and concerned with identifying optimal decision, optimal decisions for a rational agent, rather than Descriptive economics, describing how people actually make decisions. Despite this, the field is important to the study of real human behavior by Social science, social scientists, as it lays the foundations to Mathematical model, mathematically model and analyze individuals in fields such as sociology, economics, criminology, cognitive science, moral philosophy and political science. History The roots of decision theory lie in probability theory, developed by Blai ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rough Set
In computer science, a rough set, first described by Polish computer scientist Zdzisław I. Pawlak, is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the ''lower'' and the ''upper'' approximation of the original set. In the standard version of rough set theory described in Pawlak (1991), the lower- and upper-approximation sets are crisp sets, but in other variations, the approximating sets may be fuzzy sets. Definitions The following section contains an overview of the basic framework of rough set theory, as originally proposed by Zdzisław I. Pawlak, along with some of the key definitions. More formal properties and boundaries of rough sets can be found in and cited references. The initial and basic theory of rough sets is sometimes referred to as ''"Pawlak Rough Sets"'' or ''"classical rough sets"'', as a means to distinguish it from more recent extensions and generalizations. Information system framework Let I = (\mathb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Mining
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the " knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (''mining'') of data itself. It also is a buzzwo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature Selection
In machine learning, feature selection is the process of selecting a subset of relevant Feature (machine learning), features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: * simplification of models to make them easier to interpret, * shorter training times, * to avoid the curse of dimensionality, * improve the compatibility of the data with a certain learning model class, * to encode inherent Symmetric space, symmetries present in the input space. The central premise when using feature selection is that data sometimes contains features that are ''redundant'' or ''irrelevant'', and can thus be removed without incurring much loss of information. Redundancy and irrelevance are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated. Feature extraction creates new features from functions of the original features, whereas feat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Retrieval
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an Information needs, information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text search, full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications. Overview An information retrieval process begins when a user enters a query into the sys ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification (machine Learning)
When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical data, categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal data, ordinal (e.g. "large", "medium" or "small"), integer, integer-valued (e.g. the number of occurrences of a particular word in an email) or real number, real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity function, similarity or metric (mathematics), distance 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 (mathematics), function, implemented by a classification algo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rough Sets
In computer science, a rough set, first described by Polish computer scientist Zdzisław I. Pawlak, is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the ''lower'' and the ''upper'' approximation of the original set. In the standard version of rough set theory described in Pawlak (1991), the lower- and upper-approximation sets are crisp sets, but in other variations, the approximating sets may be fuzzy sets. Definitions The following section contains an overview of the basic framework of rough set theory, as originally proposed by Zdzisław I. Pawlak, along with some of the key definitions. More formal properties and boundaries of rough sets can be found in and cited references. The initial and basic theory of rough sets is sometimes referred to as ''"Pawlak Rough Sets"'' or ''"classical rough sets"'', as a means to distinguish it from more recent extensions and generalizations. Information system framework Let I = (\mathb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Granular Computing
Granular computing is an emerging computing paradigm of Data processing, information processing that concerns the processing of complex information entities called "information granulation, granules", which arise in the process of data abstraction and knowledge extraction, derivation of knowledge from information or data. Generally speaking, information granules are collections of entities that usually originate at the numeric level and are arranged together due to their Similarity measure, similarity, functional or physical adjacency, indistinguishability, coherency, or the like. At present, granular computing is more a ''theoretical perspective'' than a coherent set of methods or principles. As a theoretical perspective, it encourages an approach to data that recognizes and exploits the knowledge present in data at various levels of resolution or scales. In this sense, it encompasses all methods which provide flexibility and adaptability in the resolution at which knowledge o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fuzzy Set Theory
Fuzzy or Fuzzies may refer to: Music * Fuzzy (band), a 1990s Boston indie pop band * Fuzzy (composer), Danish composer Jens Vilhelm Pedersen (born 1939) * ''Fuzzy'' (album), 1993 debut album of American rock band Grant Lee Buffalo * "Fuzzy", a song from the 2009 '' Collective Soul'' album by Collective Soul * "Fuzzy", a song from '' Poppy.Computer'', the debut 2017 album by Poppy * Fuzzy, an Australian events company that organises Listen Out, a multi-city Australian music festival Nickname * Faustina Agolley (born 1984), Australian television presenter, host of the Australian television show ''Video Hits'' * Fuzzy Haskins (1941–2023), American singer and guitarist with the doo-wop group Parliament-Funkadelic * Fuzzy Hufft (1901−1973), American baseball player * Fuzzy Knight (1901−1976), American actor * Andrew Levane (1920−2012), American National Basketball Association player and coach * Robert Alfred Theobald (1884−1957), United States Navy rear admiral * Fuz ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
