Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TLDR
A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.Citations
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Journal ArticleDOI
FineLocator: A novel approach to method-level fine-grained bug localization by query expansion
TL;DR: The proposed FineLocator approach can improve the performances of method-level bug localization at average by 20%, 21% and 17% measured by Top-N indicator, MAP and MRR respectively, in comparison with state-of-the-art techniques.
Proceedings ArticleDOI
Classification of Data Streams Applied to Insect Recognition: Initial Results
TL;DR: The objective of this paper is to evaluate methods that adapt concept drifts by regularly updating the classification models applied to insect recognition in a data stream by showing in the initial results that the philosophy of inserting and removing examples from the training set are of essential importance.
Proceedings ArticleDOI
Many-objective genetic programming for job-shop scheduling
TL;DR: This paper focuses on evolving a set of trade-off dispatching rules for many-objective JSS, which can generate non-dominated schedules given any unseen instance and demonstrates the efficacy of the newly proposed algorithm on the tested job-shop benchmark instances.
Journal ArticleDOI
FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm.
TL;DR: An exploit prediction model based on a combination of fastText and LightGBM algorithm and called fastEmbed is proposed, which can improve the ability to describe the exploitability of vulnerabilities and predict exploits in the wild effectively.
Journal ArticleDOI
Learning-Based Disassembly Process Planner for Uncertainty Management
TL;DR: A fuzzy Petri net model is introduced to explicitly represent the dynamics inherent in disassembly and a self-adaptive disassembly process planner and associated computationally effective algorithms are designed in a way to accumulate the past experience of predicting such data and exploit the ldquoknowledgerdquo captured in the data.
References
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Journal ArticleDOI
Instance-Based Learning Algorithms
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Book
Machine Learning: An Artificial Intelligence Approach
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
TL;DR: This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
Journal ArticleDOI
Queries and Concept Learning
TL;DR: This work considers the problem of using queries to learn an unknown concept, and several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries.