Instance-Based Learning Algorithms
Machine Learning
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The principal axes method for constructive induction
ML92 Proceedings of the ninth international workshop on Machine learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Constructive induction using a non-greedy strategy for feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Linear Machine Decision Trees
Concept acquisition through representational adjustment
Concept acquisition through representational adjustment
Constructing nominal X-of-N attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
Toolkit support for developing and deploying sensor-based statistical models of human situations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Expert Systems with Applications: An International Journal
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Analytical features: a knowledge-based approach to audio feature generation
EURASIP Journal on Audio, Speech, and Music Processing
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Any time induction of decision trees: an iterative improvement approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Explanation-based feature construction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Strengthening learning algorithms by feature discovery
Information Sciences: an International Journal
Embedding monte carlo search of features in tree-based ensemble methods
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Improving business rating predictions using graph based features
Proceedings of the 19th international conference on Intelligent User Interfaces
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Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creating an accurate, succinct and comprehensible representation of the target concept. To overcome this problem, researchers have proposed algorithms for automatic construction of features. The majority of these algorithms use a limited predefined set of operators for building new features. In this paper we propose a generalized and flexible framework that is capable of generating features from any given set of constructor functions. These can be domain-independent functions such as arithmetic and logic operators, or domain-dependent operators that rely on partial knowledge on the part of the user. The paper describes an algorithm which receives as input a set of classified objects, a set of attributes, and a specification for a set of constructor functions that contains their domains, ranges and properties. The algorithm produces as output a set of generated features that can be used by standard concept learners to create improved classifiers. The algorithm maintains a set of its best generated features and improves this set iteratively. During each iteration, the algorithm performs a beam search over its defined feature space and constructs new features by applying constructor functions to the members of its current feature set. The search is guided by general heuristic measures that are not confined to a specific feature representation. The algorithm was applied to a variety of classification problems and was able to generate features that were strongly related to the underlying target concepts. These features also significantly improved the accuracy achieved by standard concept learners, for a variety of classification problems.