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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

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Machine learning in automated text categorization

The theory of rough sets and formal concept analysis are compared in a common framework based on formal contexts. Different concept lattices can be constructed. Formal concept analysis focuses on concepts that are definable by conjuctions of properties, rough set theory focuses on concepts that are definable by disjunctions of properties. They produce different types of rules summarizing knowledge embedded in data.

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A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis

A new interpretation of rules in rough set theory is introduced According to the positive, boundary, and negative regions of a set, one can make a three-way decision: accept, abstain and reject The three regions enable us to derive three types of decision rules, namely, positive rules for acceptance, boundary rules for indecision or delayed decision, and negative rules for rejection Within the decision-theoretic rough set model, the associated costs of rules are analyzed

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Three-Way Decision: An Interpretation of Rules in Rough Set Theory

Granular computing (GrC) may be regarded as a label of theories, methodologies, techniques, and tools that make use of granules, i.e., groups, classes, or clusters of a universe, in the process of problem solving. The main objective of this paper is to discuss basic issues of GrC, with emphasis on the construction of granules and computation with granules. After a brief review of existing studies, a set-theoretic model of GrC is proposed based on the notion of power algebras.

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Granular Computing: basic issues and possible solutions

An alternative formulation of rough set theory can be developed based on a binary relation between two universes, one is a finite set of objects and the other is a finite set of properties. Rough set approximation operators are defined with respect to the binary relation. Three concept lattices are constructed based on approximation operators. They are different from, but related to, the concept lattice built in formal concept analysis. Through the study of the introduced concept lattices, one can obtain an in-depth understanding of data analysis using rough set theory.

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Concept lattices in rough set theory

1: Granular Computing - A New Paradigm.- Some Reflections on Information Granulation and its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language.- 2: Granular Computing in Data Mining.- Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules.- Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems.- Validation of Concept Representation with Rule Induction and Linguistic Variables.- Granular Computing Using Information Tables.- A Query-Driven Interesting Rule Discovery Using Association and Spanning Operations.- 3: Data Mining.- An Interactive Visualization System for Mining Association Rules.- Algorithms for Mining System Audit Data.- Scoring and Ranking the Data Using Association Rules.- Finding Unexpected Patterns in Data.- Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model.- 4: Granular Computing.- Observability and the Case of Probability.- Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices.- Granular Computing with Closeness and Negligibility Relations.- Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty.- Basic Issues of Computing with Granular Probabilities.- Multi-dimensional Aggregation of Fuzzy Numbers Through the Extension Principle.- On Optimal Fuzzy Information Granulation.- Ordinal Decision Making with a Notion of Acceptable: Denoted Ordinal Scales.- A Framework for Building Intelligent Information-Processing Systems Based on Granular Factor Space.- 5: Rough Sets and Granular Computing.- GRS: A Generalized Rough Sets Model.- Structure of Upper and Lower Approximation Spaces of Infinite Sets.- Indexed Rough Approximations, A Polymodal System, and Generalized Possibility Measures.- Granularity, Multi-valued Logic, Bayes' Theorem and Rough Sets.- The Generic Rough Set Inductive Logic Programming (gRS-ILP) Model.- Possibilistic Data Analysis and Its Similarity to Rough Sets.

Yiyu Yao

Papers

A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis

Three-Way Decision: An Interpretation of Rules in Rough Set Theory

Granular Computing: basic issues and possible solutions

Concept lattices in rough set theory

Data mining, rough sets and granular computing