Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Social Network Analysis

An intuitionistic fuzzy set, characterized by a membership function and a non-membership function, is a generalization of fuzzy set. In this paper, based on score function and accuracy function, we introduce a method for the comparison between two intuitionistic fuzzy values and then develop some aggregation operators, such as the intuitionistic fuzzy weighted averaging operator, intuitionistic fuzzy ordered weighted averaging operator, and intuitionistic fuzzy hybrid aggregation operator, for aggregating intuitionistic fuzzy values and establish various properties of these operators.

Intuitionistic Fuzzy Aggregation Operators

Theory of fuzzy integrals and its applications

http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

Partial table of contents: DEMPSTER-SHAFER THEORY OF EVIDENCE: GENERAL ISSUES. Measures of Uncertainty in the Dempster-Shafer Theory of Evidence (G. Klir). Comparative Beliefs (S. Wong, et al.). Calculus with Linguistic Probabilities and Beliefs (M. Lamata & S. Moral). FUZZIFICATION OF DEMPSTER-SHAFER THEORY OF EVIDENCE. Rough Membership Functions (Z. Pawlak & A. Skowron). DEMPSTER-SHAFER THEORY IN DECISION MAKING AND OPTIMIZATION. Decision Analysis Using Belief Functions (T. Strat). Interval Probabilities Induced by Decision Problems (T. Whalen). DEMPSTER-SHAFER THEORY FOR THE MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS. Using Dempster-Shafer's Belief-Function Theory in Expert Systems (P. Shenoy). Nonmonotonic Reasoning with Belief Structures (R. Yager). Index.

Advances in the Dempster-Shafer theory of evidence

In this paper, a model for group decision-making is proposed and defined in a linguistic context. A multiperson multicriteria decision problem is considered, in which a group of experts is involved in the evaluation of the performances of a set of alternatives with respect to a predefined set of criteria. The objective is to evaluate a consensual judgement and a consensus degree on each alternative. Both the experts' evaluations of the alternatives and the degree of consensus are expressed linguistically. A "soft" consensus degree referred to a fuzzy majority of the experts is proposed based on the concept of linguistic quantifier. The entire process is defined in a linguistic domain and modeled within fuzzy set theory by ordered weighted average (OWA) operators.

A linguistic modeling of consensus in group decision making based on OWA operators

Group decision making and consensus under fuzzy preferences and fuzzy majority

A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences

Regression analysis is a relatively simple yet extremely useful and widely employed tool for determining relationship between some variables on the basis of some observed values taken by these variables. Fuzzy regression analysis has been recently deviced to accomodate in the framework of regression analysis vaguely specified data which are omnipresent in many applications, notably in all areas where human judgements are used. Fuzzy sets theory provides here proper tools. This book is a collection of papers written by virtually all major contributors to fuzzy regression. Its main issue is that vague, imprecise, etc. data may now be used in regression analysis. This is new. Apart from this it gives an extensive coverage of the whole field of fuzzy regression, both in a strictly mathematical and applicational perspective. Most approaches are algorithmic, and can be readily implemented. Information on software is provided.

Mario Fedrizzi

Papers

Advances in the Dempster-Shafer theory of evidence

A linguistic modeling of consensus in group decision making based on OWA operators

Group decision making and consensus under fuzzy preferences and fuzzy majority

A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences

Fuzzy regression analysis