Call for Chapters
“Diagnostic Test Approaches to Machine Learning and
Commonsense Reasoning Systems”
to be published by IGI Global (www.igi-global.com)
scheduled for release in 2012
Editors:
Xenia A. Naidenova, Senior Researcher at Military Medical Academy, Russian Federation, SaintPetersburg
Viktor L. Shagalov, Programmer at Speech Modules Ltd., Israel, Rehovot
Introduction, Aims & Topics
Introduction
Research on supervised symbolic machine learning is developing in many directions which are
sometimes unconnected with each other. Discussions in the literature, for example, tend to deal with
creating algorithms for solving separate, well-formulated practical problems.
A more complex approach which considers symbolic supervised machine learning methods as a class
of multifunctional inductive-deductive reasoning is important and long overdue. The processes of
symbolic machine learning are like the processes of plausible reasoning, in which the synthesis of the
logical operations—such as induction, the deduction, abduction, reasoning from the contrary,
hypotheses generation and refutation—are achieved. The interconnection between learning algorithms
and the results of diagnostic tasks based on acquired knowledge presents many problems which need
to be addressed. These problems include approaches to analyzing the results from the users’
perspective and reconstructing learning algorithms in order to improve the subsequent pattern
recognition results.
The consideration of symbolic machine learning algorithms as an entire class will make it possible, in
the future, to generate algorithms (with the aid of some parameters) depending on the initial users’
requirements and the quality of solving targeted problems in domain applications.
Aims
The objectives of this book are:
1. To survey, analyze, and compare the existing and most effective algorithms for mining all
kinds of logical rules, including Apriori-like bottom-up search, Formal Concept Analysis, closure
operations of Galois connections, and Diagnostic Test Approach and some others.
2. To show how these approaches use shared concepts for mining logical rules, including item,
item set, transaction, frequent itemset, maximal itemset, generator (non-redundant or irredundant
itemset), closed itemset, support, and confidence.
3. To consider all these approaches based on the same mathematical language (the lattice theory)
and to analyze all the techniques in constructing rule mining algorithms.
4. To describe the genesis of rule mining algorithms and to give the classification of these
algorithms with respect to 1) various kinds of branching ordering methods, 2) various kinds of
pruning methods, 3) various techniques (inductive methods) of algorithm constructing (for example,
simultaneously exploring both the itemset space and transaction space, decomposing the main task in
the sub-problems using item- or itemset- projection, item or itemset skipping techniques, itemset-tidset tree search space, and FP-tree search space).
5. To focus on using language technologies in learning by trying to identify what
questions and problems are solved, but also show how well the algorithms developed assist in
the provision of support and the construction of feedback for learning.
Audience
The primary goal of the book is to bring together experts in the related fields in order to:
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Share knowledge (i.e., approaches, models, issues, solutions) acquired in the domain
of using supervised symbolic machine learning methods;
Present applications in different domains;
Debate on directions and possibilities of future research in the domain;
Create a forum for further collaboration and develop an international community on
this field of study.
Topics
Chapters should cover three directions in the development of symbolic supervised machine
learning methods:
1. Theoretical problems in constructing supervised symbolic machine learning algorithms
elaborated in the framework of formal model of conceptual reasoning, based on an algebraic
lattice;
2. Presentation and description of integrated multifunctional machine learning systems; and
3. Applying the described machine learning systems and algorithms for solving some practical
and rather complex diagnostic tasks.
Suggested topics include, but are not limited to, the following:
The Logical or Symbolic Methods of Machine Learning: Current Trends
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Machine Learning and Extracting Conceptual Knowledge from Data
Supervised Symbolic Learning as Inferring Logical Dependencies from Data
Unsupervised Symbolic Machine Learning (Conceptual Clustering) as Inferring Natural
Classification from Data
The Interconnection Supervised with Unsupervised Symbolic Learning
Ontology Application to Knowledge Discovery Process Based on Integrating Supervised and
Unsupervised Conceptual Machine Learning
The synthesis of cognitive procedures and the problem of induction
Learning Logic Formulas
Logic formulas based knowledge discovery
Feature Selection for Data Mining
Classification Algorithm for filtering Information of the Web Pages in Search Engines
Data Mining and Knowledge Discovery Via Logic-Based Methods
An Analytical Survey of Current Approaches to Mining Logical Rules from Data
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The Main Current Approaches to Mining Logical Rules from Data
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Apriori-Like Approach to Inferring Functional, Implicative Dependencies, and Association
Rules
Formal Concept Analysis Approach to Inferring Functional, Implicative Dependencies, and
Association Rules
JSM Method of Machine Learning (automated hypotheses generation)
Galois Connections and Closure Operations for Inferring Functional, Implicative
Dependencies, and Association Rules
Diagnostic Test Approach to Inferring Functional, Implicative Dependencies, and Association
Rules
An approach to Machine Leaning Based on Inferring Boolean Functions from Positive and
Negative Examples
Integration of Mining All Types of Logical Rules Based on Operations of Data-Knowledge
Lattice Construction
Effective Current Algorithms and Techniques for Symbolic Machine Learning
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Incremental Algorithms for All Kind of Frequent Itemsets Construction
Constructing Galois Lattices as a Commonsense (plausible) Reasoning Process
Incremental Construction of Concept Lattices
Plausible reasoning in the systems of JSM type
The genesis of rule mining algorithms and the classification of these algorithms with
respect to 1) various kinds of branching ordering methods, 2) various kinds of pruning
methods, 3) various techniques (inductive methods) of algorithm constructing, for
example, simultaneously exploring both the itemset space and transaction space,
decomposing the main task in the sub-problems using item- or itemset- projection,
item or itemset skipping techniques, itemset-tid-set tree search space, and FP-tree
search space and some others methods
Fast Heuristics for Inferring a Boolean Function from Examples
An Incremental Learning Algorithm for Inferring Boolean Functions
Symbolic Machine Learning Program Systems
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Machine Learning as an Instrument for Intelligent System Organization
Diagnostic Test Machine (DTM)
Program Realization of the systems of JSM type
Mechanisms of Data and Knowledge Management in Machine Leaning Systems
Application of Symbolic Machine Learning Methods
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Concept Lattices and Their Applications
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Application of DTM in Psycho-Diagnostics
Application of DTM for Diagnosing Schizophrenia and Mania Disorders Based on Patient
Texts Analysis
Some Realistic Applications of Inferring a Boolean Function from Examples (Breast
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Cancer Diagnosis, Predicting Muscle Fatigue from EMG Signals)
Intelligent System of JSM Type for Analyzing Clinical Data in Oncology
Machine learning Methods for Analyzing Medical Data
Intelligent Analysis of Data in Sociology
Logic Classification and Feature Selection for Biomedical Data
Logic Based Methods for SNPs Tagging and Reconstruction
Machine Learning in Web Search Engine
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Web Mining in Thematic Search Engine
The Analysis of Service Quality via Stated Preferences and Rule-Based Classification
Learning and Reasoning
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Ontology Environment for Knowledge Acquisition
Incorporating Machine Learning in a Plausible Reasoning Environment for Realizing
Conceptual Communication of Domain Experts with Intelligent Machine Learning Systems
Feedback from Quality of Decision Making Results to Machine Learning Algorithm
Construction
The Building of High-Performance Web search engines Capable to Adapt to the Evolution of
Users' Information Needs, Expectations and Behavior
Natural Language Interface to Symbolic Machine Learning Algorithms
Chapter Format Requirements and Publication Details
Submission Types
Submitted chapters should describe substantial and unpublished work and should be
submitted in English. Submissions are expected to have either 7,000-9,000 words for short
papers (presenting very new and not so formalized work) or 17,000-19,000 words for long
chapters, (presenting theoretical or completed applied work).
Chapter Submission Process
Researchers and practitioners are invited to submit on or before November 28, 2010, a 2-3 page
chapter proposal clearly explaining the mission and concerns of his or her proposed chapter.
Authors of accepted proposals will be notified about the status of their proposals and sent chapter
guidelines. Full chapters are to be submitted by February 6, 2011.
All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be
requested to serve as reviewers for this project.
Important Dates
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Submission Deadline : November 28, 2010
Notification of acceptance: December 16, 2010
Full chapter due: February 6, 2011
Publication-ready papers due date: May 31, 2011
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Editorial Advisory Board:
Giovanni Felici, Istituto Dianalisi dei Sistemi ed Informatica “Antonio Ruberti”, Consiglio Nazionale
Delle Ricerche, Rome, Italy
Evangelos Triantaphyllou, Department of Computer Science, Louisiana State University, USA
Sergei O. Kusnetsov, Department of Applied Mathematics, State University Higher School of
Economics, Russian Federation, Moscow
Viktor K. Finn, Department of Theoretical and Applied Problems of the Information Theory, Russian
Academy of Science, Moscow
Sergei A. Obiedkov, Department of Applied Mathematics, State University Higher School of
Economics, Moscow, Russian Federation
Omar Larouk, National School of Advanced Studies in Information Science and Libraries, France
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher
of the “Information Science Reference” (formerly Idea Group Reference) imprint. For
additional information regarding the publisher, please visit www.igi-global.com. This
publication is scheduled for release in early 2012.
Inquiries and submissions can be forwarded electronically (Word document):
Xenia A. Naidenova, naidenovaxen@gmail.com
Viktor L. Shagalov, shagalovv@gmail.com