Nick Cercone

Data-driven discovery of quantitative rules in relational databases

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases. >

LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH

Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge‐based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute‐oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine‐learning paradigm, especially learning‐from‐examples techniques, with rough set techniques. An attribute‐oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause‐effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute‐oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in database systems.

N-gram-based detection of new malicious code

The current commercial anti-virus software detects a virus only after the virus has appeared and caused damage. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, we explore the idea of automatically detecting new malicious code using the collected dataset of the benign and malicious code. We obtained accuracy of 100% in the training data, and 98% in 3-fold cross-validation.

Discovering rules for water demand prediction: An enhanced rough-set approach☆

Abstract Prediction of consumer demands is a pre-requisite for optimal control of water distribution systems because minimum-cost pumping schedules can be computed if water demands are accurately estimated. This paper presents an enhanced rough-sets method for generating prediction rules from a set of observed data. The proposed method extends upon the standard rough set model by making use of the statistical information inherent in the data to handle incomplete and ambiguous training samples. It also discusses some experimental results from using this method for discovering knowledge on water demand prediction.

Intelligent query answering by knowledge discovery techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems.

What is knowledge representation

In this chapter, we overview eight major approaches to knowledge representation: logical representations, semantic networks, procedural representations, logic programming formalisms, frame-based representations, production system architectures, and knowledge representation languages. The fundamentals of each approach are described, and then elaborated upon through illustrative examples chosen from actual systems which employ the approach. Where appropriate, comparisons among the various schemes are drawn. The chapter concludes with a set of general principles which have grown out of the different approaches.

RULE QUALITY MEASURES FOR RULE INDUCTION SYSTEMS: DESCRIPTION AND EVALUATION

A rule quality measure is important to a rule induction system for determining when to stop generalization or specialization. Such measures are also important to a rule‐based classification procedure for resolving conflicts among rules. We describe a number of statistical and empirical rule quality formulas and present an experimental comparison of these formulas on a number of standard machine learning datasets. We also present a meta‐learning method for generating a set of formula‐behavior rules from the experimental results which show the relationships between a formulas performance and the characteristics of a dataset. These formula‐behavior rules are combined into formula‐selection rules that can be used in a rule induction system to select a rule quality formula before rule induction. We will report the experimental results showing the effects of formula‐selection on the predictive performance of a rule induction system.

Rule-induction and case-based reasoning: hybrid architectures appear advantageous

Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule induction methods with case-based reasoning techniques to engender performance improvements over more traditional single-representation architectures. We briefly survey several major rule-induction and case-based reasoning ML systems. We then examine some interesting hybrid combinations of these systems and explain their strengths and weaknesses as learning systems. We present a balanced approach to constructing a hybrid architecture, along with arguments in favor of this balance and mechanisms for achieving a proper balance. Finally, we present some initial empirical results from testing our ideas and draw some conclusions based on those results.

An attribute-oriented approach for learning classification rules from relational databases

A classification rule is a rule which characterizes the properties that distinguish one class from other classes. An attribute-oriented induction algorithm which extracts classification rules from relational databases is developed. The algorithm adopts the artificial intelligence learning from examples paradigm and applies an attribute-oriented concept tree ascending technique in the learning process. The technique integrates database operations with the learning process and provides a simple and efficient way of learning from large databases. The algorithm learns both conjunctive rules and restricted forms of disjunctive rules. Using database statistics, learning can be performed on databases containing noisy data and exceptions. An analysis and comparison with other algorithms show that attribute-oriented induction substantially reduces the complexity of database learning processes.<<ETX>>

THE STRUCTURE AND ORGANIZATION OF A SEMANTIC NET FOR COMPREHENSION AND INFERENCE

We have developed a network representation for propositional knowledge that we believe to be capable of encoding any proposition expressible in natural language. The representation can be regarded as a computer-oriented logic with associative access paths from concepts to propositions. Its syntax is closely modeled on predicate calculus but includes constructs for expressing some kinds of vague and uncertain knowledge. The representation allows the encoding and efficient use of caselike semantic constraints on predicate arguments for the purpose of language comprehension: these constraints are simply implications of the predicates concerned. Our approach to language comprehension is based on nonprimitive representations. We argue that primitive representations of simple propositions are often extremely complex, and offer no real advantages. We have demonstrated these ideas with a mini-implementation capable of mapping certain kinds of declarative sentences into the network representation. The implementation emphasizes the proper handling of iterated adjectival modifiers, especially comparative modifiers. More recently, we have worked on the problem of rapid access to the facts that are relevant to a query. Our solution involves the use of back-link structures from concepts to propositions, called “topic access skeletons,” which conform with general topic hierarchies in memory. For example, the proposition “Clyde is grey” is classified under the “coloring” topic for Clyde, which is subsumed under the “appearance” topic, and in turn under the “external quality” topic, and finally under the “physical quality” topic for Clyde. The form of a query (or of an assertion) can be used to determine what concepts in memory should be accessed as starting points, and what paths in the associated access skeletons should be followed in order to access the relevant information. We have demonstrated the feasibility of building such hierarchies, inserting information into them automatically, and accessing the inserted information with a second experimental implementation. The hierarchic organization appears capable of providing order-of-magnitude improvements in question-answering efficiency, with only a doubling in storage costs.