For Instance
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Instance Selection and Construction for Data Mining
Author: Huan Liu
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-09
The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.
Oxford Handbook of Internet Psychology
Over one billion people use the Internet globally. Psychologists are beginning to understand what people do online, and the impact being online has on behaviour. It's making us re-think many of our existing assumptions about what it means to be a social being. For instance, if we can talk, flirt, meet people and fall in love online, this challenges many of psychology's theories that intimacy or understanding requires physical co-presence. "The Oxford Handbook of Internet Psychology" brings together many of the leading researchers in what can be termed 'Internet Psychology'. Though a very new area of research, it is growing at a phenomenal pace. In addition to well-studied areas of investigation, such as social identity theory, computer-mediated communication and virtual communities, the volume also includes chapters on topics as diverse as deception and misrepresentation, attitude change and persuasion online, Internet addiction, online relationships, privacy and trust, health and leisure use of the Internet, and the nature of interactivity. With over 30 chapters written by experts in the field, the range and depth of coverage is unequalled, and serves to define this emerging area of research. Uniquely, this content is supported by an entire section covering the use of the Internet as a research tool, including qualitative and quantitative methods, online survey design, personality testing, ethics, and technological and design issues. While it is likely to be a popular research resource to be 'dipped into', as a whole volume it is coherent and compelling enough to act as a single text book. "The Oxford Handbook of Internet Psychology" is the definitive text on this burgeoning field. It will be an essential resource for anyone interested in the psychological aspects of Internet use, or planning to conduct research using the 'net'.
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations
This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).