Modified Average of the Base-Level Models in the Hill-Climbing Bagged Ensemble Selection Algorithm for Credit Scoring

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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Forest Pruning Based on Branch Importance

Computational Intelligence and Neuroscience ◽

10.1155/2017/3162571 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Xiangkui Jiang ◽

Chang-an Wu ◽

Huaping Guo

Keyword(s):

Decision Tree ◽

Decision Trees ◽

Selection Algorithm ◽

Ensemble Size ◽

Ensemble Pruning ◽

Generalization Ability ◽

Ensemble Selection ◽

Novel Strategy

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.

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Adaptive fitness landscape for replicator systems: to maximize or not to maximize

Mathematical Modelling of Natural Phenomena ◽

10.1051/mmnp/2018040 ◽

2018 ◽

Vol 13 (3) ◽

pp. 25 ◽

Cited By ~ 2

Author(s):

Alexander S. Bratus ◽

Yuri S. Semenov ◽

Artem S. Novozhilov

Keyword(s):

Natural Selection ◽

Fitness Landscape ◽

Evolutionary Process ◽

Hill Climbing ◽

Adaptive Landscape ◽

Basic Model ◽

Mean Fitness ◽

The Mean ◽

The Hill ◽

Fisher’S Fundamental Theorem

Sewall Wright’s adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher’s fundamental theorem of natural selection and Kimura’s maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a severe misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system’s dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system’s dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.

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Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

Journal of Information Technology Research ◽

10.4018/jitr.2019100108 ◽

2019 ◽

Vol 12 (4) ◽

pp. 153-170 ◽

Cited By ~ 1

Author(s):

Guefrouchi Ryma ◽

Kholladi Mohamed-Khireddine

Keyword(s):

Genetic Algorithm ◽

Optimization Problem ◽

Ontology Mapping ◽

Hill Climbing ◽

Computational Time ◽

Mutation Operator ◽

Mapping Process ◽

The Hill

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

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