A5086906175- Data Mining Algorithms and Applications
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
- Rough Sets and Fuzzy Logic
- Semantic Web and Ontologies
- Data Management and Algorithms
- Advanced Database Systems and Queries
- Machine Learning and Algorithms
- Natural Language Processing Techniques
- Logic, Reasoning, and Knowledge
- Data Stream Mining Techniques
- Advanced Clustering Algorithms Research
- AI-based Problem Solving and Planning
- Neural Networks and Applications
- Bioinformatics and Genomic Networks
- Topic Modeling
- Machine Learning in Bioinformatics
- Algorithms and Data Compression
- Time Series Analysis and Forecasting
- Imbalanced Data Classification Techniques
- Genomics and Phylogenetic Studies
- Computational Drug Discovery Methods
- Text and Document Classification Technologies
- Image Retrieval and Classification Techniques
- Advanced Graph Neural Networks
KU Leuven
2015-2024
Leiden University
2008-2016
Czech Academy of Sciences, Institute of Computer Science
1996-2015
Saarland University
1996-2010
University of Alberta
2010
University of Siena
2006
Polish Academy of Sciences
1996
Institute of Computer Science
1996
University of North Carolina at Charlotte
1996
Oak Ridge National Laboratory
1996
article Free Access Share on Web mining research: a survey Authors: Raymond Kosala Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium BelgiumView Profile , Hendrik Blockeel Authors Info & Claims ACM SIGKDD Explorations NewsletterVolume 2Issue 1June, 2000 pp 1–15https://doi.org/10.1145/360402.360406Published:01 June 2000Publication History 813citation14,361DownloadsMetricsTotal Citations813Total Downloads14,361Last 12 Months480Last 6...
An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs principles instance based learning. The resulting methodology implemented in TIC (Top down Induction Clustering trees) system for first order logical tree representation inductive logic programming Tilde. Various experiments with are presented, both propositional and relational domains.
S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions ORFs these automatically. Different machine learning have been proposed this end, but it remains unclear which method be preferred terms predictive performance, efficiency usability. We study use decision tree based models for predicting multiple ORFs. First, we...
With the huge amount of information available online, World Wide Web is a fertile area for data mining research. The research at cross road from several communities, such as database, retrieval, and within AI, especially sub-areas machine learning natural language processing. However, there lot confusions when comparing efforts different point views. In this paper, we survey in mining, out some regarded usage term suggest three categories. Then situate with respect to these We also explore...
Thousands of machine learning research papers contain extensive experimental comparisons. However, the details those experiments are often lost after publication, making it impossible to reuse these in further research, or reproduce them verify claims made. In this paper, we present a collaboration framework designed easily share with community, and automatically organize public databases. This enables immediate for subsequent, possibly much broader investigation offers faster more thorough...
Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation the its computational overhead. In this paper we show that, for trees, overhead cross-validation can be reduced significantly by integrating with normal induction process. We discuss how existing algorithms adapted to aim, provide an analysis speedups these adaptations may yield. The supported...
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning data mining. However, in order ILP to become practically useful, the efficiency of systems must improve substantially. To this end, notion query pack introduced: it structures sets similar queries. Furthermore, mechanism described executing such packs. A complexity analysis shows that considerable improvements can be achieved through use execution mechanism. This claim supported by empirical...
We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing learners few crucial, well-motivated details. Experiments on synthetic and real-life datasets confirm beneficial effect of these differences show that resulting system outperforms learners.
Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform more efficiently. More specifically, they identify groups interchangeable variables and once per group, as opposed variable. The are defined by means constraints, so flexibility grouping is determined expressivity constraint language. Existing approaches for exact lifted use specific languages (in)equality which often have limited expressivity. In this article, we decouple from We...
Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within genome, increasing genome size contributing to genetic diversity across species. Accurate identification classification TEs present in is an important step towards understanding their effects on genes role evolution. We introduce TE-Learner, framework based machine learning automatically identifies given assigns them. implementation our LTR...