FOIL: A midterm report

Quinlan, J. R.; Cameron-Jones, R. Mike

doi:10.1007/3-540-56602-3_124

Cited by 320 publications

(204 citation statements)

References 7 publications

(9 reference statements)

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“…Inductive Learner) is an inductive learning algorithm for generating Classification Association Rules (CARs) developed by Quinlan and Cameron-Jones [22]. This algorithm was later further developed by Yin and Han to produce the PRM (Predictive Rule Mining) CAR generation algorithm PRM was then further developed, by Yin and Han, to produce CPAR (Classification based on Predictive Association Rules) [28].…”

Section: Foil -Cpar -Prm: Foil (First Ordermentioning

confidence: 99%

Arguing from Experience to Classifying Noisy Data

Wardeh

Coenen

Bench‐Capon

2009

Data Warehousing and Knowledge Discovery

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Abstract.A process, based on argumentation theory, is described for classifying very noisy data. More specifically a process founded on a concept called "arguing from experience" is described where by several software agents "argue" about the classification of a new example given individual "case bases" containing previously classified examples. Two "arguing from experience" protocols are described: PADUA which has been applied to binary classification problems and PISA which has been applied to multi-class problems. Evaluation of both PADUA and PISA indicates that they operate with equal effectiveness to other classification systems in the absence of noise. However, the systems out-perform comparable systems given very noisy data.

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Section: Foil -Cpar -Prm: Foil (First Ordermentioning

confidence: 99%

Arguing from Experience to Classifying Noisy Data

Wardeh

Coenen

Bench‐Capon

2009

Data Warehousing and Knowledge Discovery

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“…There are many studies on relational (or first-order) classification [1,9,10,14], which aims at building accurate classifiers in relational databases. Such algorithms search among different relations for useful predicates, by transferring information across relations.…”

Section: Related Workmentioning

confidence: 99%

“…As many approaches on relational (or first-order) classification [1,9,10,14], MDBM also uses rule-based classification. All previous approaches build rules by searching for predicates (or literals) with highest information gain (or Foil gain), in order to build accurate rules.…”

Section: Introductionmentioning

confidence: 99%

Efficient Classification from Multiple Heterogeneous Databases

Yin

2005

Lecture Notes in Computer Science

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Abstract. With the fast expansion of computer networks, it is inevitable to study data mining on heterogeneous databases. In this paper we propose MDBM, an accurate and efficient approach for classification on multiple heterogeneous databases. We propose a regression-based method for predicting the usefulness of inter-database links that serve as bridges for information transfer, because such links are automatically detected and may or may not be useful or even valid. Because of the high cost of inter-database communication, MDBM employs a new strategy for cross-database classification, which finds and performs actions with high benefit-to-cost ratios. The experiments show that MDBM achieves high accuracy in cross-database classification, with much higher efficiency than previous approaches.

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“…FOIL [28] is a first-order learning system which can generate Horn rules for target predicates by examining both positive and negative examples. WARMER [6] is designed for frequent pattern mining in relational databases.…”

Section: Related Workmentioning

confidence: 99%

Discovering Executable Semantic Mappings Between Ontologies

Qin

Dou

LePendu

On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS

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Abstract.Creating executable semantic mappings is an important task for ontology-based information integration. Although it is argued that mapping tools may require interaction from humans (domain experts) for best accuracy, in general, automatic ontology mapping is an AI-Complete problem. Finding matchings (correspondences) between the concepts of two ontologies is the first step towards solving this problem but matchings are normally not directly executable for data exchange or query translation. This paper presents an systematic approach to combining ontology matching, object reconciliation and multi-relational data mining to find the executable mapping rules in a highly automatic manner. Our approach starts from an iterative process to search the matchings and do object reconciliation for the ontologies with data instances. Then the result of this iterative process is used for mining frequent queries. Finally the semantic mapping rules can be generated from the frequent queries. The results show our approach is highly automatic without losing much accuracy compared with human-specified mappings.

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FOIL: A midterm report

Cited by 320 publications

References 7 publications

Arguing from Experience to Classifying Noisy Data

Arguing from Experience to Classifying Noisy Data

Efficient Classification from Multiple Heterogeneous Databases

Discovering Executable Semantic Mappings Between Ontologies

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