Learning Logical Definitions from Relations

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Classification And Regression Trees

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Simplifying decision trees

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The CN2 Induction Algorithm

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A Universal Prior for Integers and Estimation by Minimum Description Length

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Explanation-Based Generalization: A Unifying View

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Expert systems in the micro-electronic age

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Algorithmic Program Debugging

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Modelling and forecasting interest rates during stages of the economic cycle: A knowledge-discovery approach

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An algorithm for learning with probabilistic description logics

Ochoa-Luna, J. E., & Cozman, F. G. (2009) · Cited by 16

Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms

Nada Lavrač, A., Fürnkranz, J., & Gamberger, D. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05177-7_6 · Cited by 16

Towards Using Knowledge Discovery Techniques in Database Marketing for the Tourism Industry

Cho, V., & Leung, P. (2002) · Journal of Quality Assurance in Hospitality & Tourism · doi:10.1300/j162v03n03_07 · Cited by 16

On the connection between the phase transition of the covering test and the learning success rate in ILP

Alphonse, É., & Osmani, A. (2007) · Machine Learning · doi:10.1007/s10994-007-5031-9 · Cited by 16

CTC — Correlating Tree Patterns for Classification

Zimmermann, A., & Bringmann, B. (2006) · Proceedings · doi:10.1109/icdm.2005.49 · Cited by 16

Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors

Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2018) · doi:10.1184/r1/6469832 · Cited by 16

A Rule-Based System to Extract Financial Information

Sheikh, M., & Conlon, S. J. (2015) · Journal of Computer Information Systems · doi:10.1080/08874417.2012.11645572 · Cited by 16

Query Optimization in Inductive Logic Programming by Reordering Literals

Struyf, J., & Blockeel, H. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39917-9_22 · Cited by 16

Prototyping structural descriptions: An inductive learning approach

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Geographic Information System Data for Supporting Feature Extraction from High-Resolution Aerial and Satellite Images

Guienko, G., & Yerach Doytsher, A. (2003) · Journal of Surveying Engineering · doi:10.1061/(asce)0733-9453(2003)129:4(158 · Cited by 16

Can user models be learned at all? Inherent problems in machine learning for user modelling

MÜLLER, M. E. (2004) · The Knowledge Engineering Review · doi:10.1017/s0269888904000141 · Cited by 16

Filtering Multi-Instance Problems to Reduce Dimensionality in Relational Learning

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Symbolic vs. connectionist learning: an experimental comparison in a structured domain

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Cryptographic limitations on learning one-clause logic programs

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Inductive specification recovery: Understanding software by learning from example behaviors

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Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding

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Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction

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Efficiently constructing relational features from background knowledge for inductive machine learning

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Learning procedural planning knowledge in complex environments

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INSS : un système hybride neuro-symbolique pour l'apprentissage automatique constructif

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A Descriptive Study of Credit Card Fraud Pattern

Jha, S., & Westland, J. C. (2013) · Global Business Review · doi:10.1177/0972150913494713 · Cited by 15

Integrating Knowledge-Driven and Data-Driven Approaches for the Derivation of Clinical Prediction Rules

Kwiatkowska, M., Atkins, A. S., Ayas, N., & Ryan, C. F. (2006) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2005.41 · Cited by 15

Combining Vector Space Embeddings with Symbolic Logical Inference over Open-Domain Text.

Gardner, M., Talukdar, P. P., & Mitchell, T. M. (2015) · Cited by 15

A new relational Tri-training system with adaptive data editing for inductive logic programming

Li, Y., & Guo, M. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.04.021 · Cited by 15

Automatic Detection of Bronchial Dilatation in HRCT Lung Images

Prasad, M. N., Sowmya, A., & Wilson, P. (2008) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-008-9113-4 · Cited by 15

Machine learning for software engineering: case studies in software reuse

Stefano, J. S. D., & Menzies, T. (2003) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/tai.2002.1180811 · Cited by 15

Quantification of protein group coherence and pathway assignment using functional association

Chitale, M., Palakodety, S., & Kihara, D. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-373 · Cited by 15

Parallel symmetric class expression learning

Tran, A. C., Dietrich, J. B., Guesgen, H. W., & Marsland, S. (2017) · Journal of Machine Learning Research · doi:10.5555/3122009.3176808 · Cited by 15

Rule Learning in a Nutshell

Fürnkranz, J., Gamberger, D., & Nada Lavrač, A. (2012) · Cognitive technologies · doi:10.1007/978-3-540-75197-7_2 · Cited by 15

Learning Logic Programs with Neural Networks

Basílio, R., Zaverucha, G., & Barbosa, V. C. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44797-0_2 · Cited by 15

From extensional to intensional knowledge: Inductive logic programming techniques and their application to deductive databases

Flach, P. A. (1998) · Lecture notes in computer science · doi:10.1007/bfb0055506 · Cited by 15

Modelling affective-based music compositional intelligence with the aid of ANS analyses

Sugimoto, T., Legaspi, R., Ota, A., Moriyama, K., Kurihara, S., & Numao, M. (2007) · Knowledge-Based Systems · doi:10.1016/j.knosys.2007.11.010 · Cited by 15

Learning an Approximation to Inductive Logic Programming Clause Evaluation

DiMaio, F., & SHAVLIK, J. W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30109-7_10 · Cited by 15

The acquisition of inductive constraints

Tenenbaum, J. B., & Kemp, C. (2008) · Cited by 15

Discovering expressive transformation rules from saxophone jazz performances

Ramírez, R., Hazan, A., Gómez, E., Maestre, E., & Serra, X. (2005) · Journal of New Music Research · doi:10.1080/09298210600578097 · Cited by 15

Towards a new concept of software

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Exchanging Data Mining Models with the Predictive Modelling Markup Language

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The communication of inductive inferences

Davies, W. E., & Edwards, P. J. (1997) · Lecture notes in computer science · doi:10.1007/3-540-62934-3_51 · Cited by 15

Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL

Džeroski, S., & Nada Lavrač, A. (1991) · Elsevier eBooks · doi:10.1016/b978-1-55860-200-7.50082-9 · Cited by 15

Approximate Match of Rules Using Backpropagation Neural Networks

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A buyer's agent

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Probabilistic First-Order Theory Revision from Examples

Paes, A., Revoredo, K. C., Zaverucha, G., & Costa, V. S. (2005) · Lecture notes in computer science · doi:10.1007/11536314_18 · Cited by 15

Background knowledge and declarative bias in inductive concept learning

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Selective Reformulation of Examples in Concept Learning

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Programming robots by example

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Automatic Generation and Checking of Program Specifications

Nimmer, J. W. (2002) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 15

Exploiting label semantics for rule-based activity recommendation in business process modeling

Sola, D., van der Aa, H., Meilicke, C., & Stuckenschmidt, H. (2022) · Information Systems · doi:10.1016/j.is.2022.102049 · Cited by 14

Refinement of rule sets with JoJo

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Improving Statistical Approach for Memory Leak Detection Using Machine Learning

Šor, V., Ou, P., Treier, T., & Srirama, S. N. (2013) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2013.92 · Cited by 14

Learning to "Read Between the Lines" using Bayesian Logic Programs

Raghavan, S., Mooney, R. J., & Ku, H.-S. (2012) · Cited by 14

Development of semantic decision tree

Jeon, D., & Kim, W. (2011) · Cited by 14

Discovering relational specifications

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A Rule-Based Recommendation Approach for Business Process Modeling

Sola, D., Meilicke, C., van der Aa, H., & Stuckenschmidt, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79382-1_20 · Cited by 14

Relation Acquisition using Word Classes and Partial Patterns

Saeger, S. D., Torisawa, K., Tsuchida, M., Kazama, J., Hashimoto, C., Yamada, I., Oh, J. H., Varga, I., & Yan, Y. (2011) · Cited by 14

Inductive Lexical Learning of Class Expressions

Bühmann, L., Fleischhacker, D., Lehmann, J., Melo, A., & Völker, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13704-9_4 · Cited by 14

Mining Massive Relational Databases

Hulten, G., Domingos, P., & Abe, Y. (2003) · Cited by 14

The use of habitat and dispersal models in protecting European black poplar ( Populus nigra L.) from genetic introgression in Slovenia

Debeljak, M., Ficko, A., & Brus, R. (2015) · Biological Conservation · doi:10.1016/j.biocon.2015.02.004 · Cited by 14

Determination of Event Patterns for Complex Event Processing Using Fuzzy Unordered Rule Induction Algorithm with Multi-objective Evolutionary Feature Subset Selection

Mehdiyev, N., Krumeich, J., Werth, D., & Loos, P. (2016) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.216 · Cited by 14

Towards Reliable Predictive Process Monitoring

Klinkmüller, C., van Beest, N., & Weber, I. M. (2018) · Lecture notes in business information processing · doi:10.1007/978-3-319-92901-9_15 · Cited by 14

An Introduction to Inductive Logic Programming and Learning Language in Logic

Džeroski, S., Cussens, J., & Manandhar, S. (2000) · Lecture notes in computer science · doi:10.1007/3-540-40030-3_1 · Cited by 14

SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model

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Artificial Intelligence in 3-D Feature Extraction

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AL-QuIn

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Interpretation of self-organizing maps with fuzzy rules

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Mining clusters and corresponding interpretable descriptions – a three–stage approach

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Bipolar version space learning

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Machine-learning techniques for software product quality assessment

Lounis, H., & Ait-Mehedine, L. (2004) · Asia-Pacific Conference on Quality Software (APAQS) · doi:10.1109/qsic.2004.1357950 · Cited by 14

Ideal refinement of Datalog programs

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Accounting for domain knowledge in the construction of a generalization space

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Comparative results on using inductive logic programming for corpus-based parser construction

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Sources of Success for Boosted Wrapper Induction

Kauchak, D., Smarr, J., & Elkan, C. (2004) · Cited by 14

The Effect of Sample Size on Different Failure Prediction Methods

Back, B., Laitinen, T., Hekanaho, J., & Sere, K. (1998) · Cited by 14

A Hybrid Approach t Word Segmentation

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Introduction: data mining

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Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach

Hazra, R., & Raedt, L. D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43421-1_13 · Cited by 13

SURVFIT: Doubly sparse rule learning for survival data

Shakur, A. H., Huang, S., Qian, X., & Chang, X. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103691 · Cited by 13

An Analysis of Stopping and Filtering Criteria for Rule Learning

Fürnkranz, J., & Flach, P. A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30115-8_14 · Cited by 13

An improved random decision trees algorithm with application to land cover classification

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A comparative study on ILP-based concept discovery systems

Kavurucu, Y., Senkul, P., & Toroslu, İ. H. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.03.038 · Cited by 13

Learning Over Dirty Data Without Cleaning

Picado, J., Davis, J., Termehchy, A., & Lee, G. Y. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389708 · Cited by 13

Generalization and specialization strategies for learning r.e. languages

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IREP++, a Faster Rule Learning Algorithm

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A unifying framework for concept-learning algorithms

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Toward General-Purpose Learning for Information Extraction

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Knowledge-based systems verification: A machine learning-based approach

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Trading off coverage for accuracy in forecasts: Applications to clinical data analysis

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Top-down induction of logic programs from incomplete samples

Inuzuka, N., Kamo, M., Ishii, N., Seki, H., & Itoh, H. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63494-0_60 · Cited by 13

Database management and analysis tools of machine induction

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A knowledge base for finite element mesh design

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Learning strategy knowledge incrementally

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Concept Formation in Context

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Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning

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Embedded machine learning systems for natural language processing: A general framework

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An investigation on the use of machine learned models for estimating correction costs

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Two methods for improving inductive logic programming systems

Stahl, I., Tausend, B., & Wirth, R. (1993) · Lecture notes in computer science · doi:10.1007/3-540-56602-3_126 · Cited by 13

LEARNING PLAYING STRATEGIES IN CHESS

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Learning Guided Automated Reasoning: A Brief Survey

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Extração de regras de classificação a partir de redes neurais para auxílio à tomada de decisão na concessão de crédito bancário

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Cooperative evolutive concept learning: an empirical study

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An Inductive Principle for Learning Logical Definitions from Relations

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Traps and pitfalls when learning logical definitions from relations

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Traps and pitfalls when learning logical theories : a case study with FOIL and FOCL

Semeraro, G. M., Brunk, C. A., & Pazzani, M. J. (1993) · eScholarship (California Digital Library) · Cited by 8

Induction of logic programs: FOIL and related systems

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FOIL: A midterm report

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