Evolutionary selection of interesting class association rules using genetic relation algorithm

2011 ◽  
Vol 6 (5) ◽  
pp. 431-440 ◽  
Author(s):  
Eloy Gonzales ◽  
Shingo Mabu ◽  
Karla Taboada ◽  
Kaoru Shimada ◽  
Kotaro Hirasawa
Keyword(s):  
Association Rules ◽  
Evolutionary Selection ◽  
Genetic Relation ◽  
Interesting Class ◽  
Selection Of

Evolutionary selection of neural networks satisfying leave-one-out criteria

2006 ◽  
Author(s):  
Giovanni Nardinocchi ◽  
Stanislaw Jankowski ◽  
Marco Balsi
Keyword(s):  
Neural Networks ◽  
Evolutionary Selection ◽  
Leave One Out ◽  
Selection Of

scholarly journals Arabic Query Expansion Using WordNet and Association Rules

2016 ◽  
Vol 12 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ahmed Abbache ◽  
Farid Meziane ◽  
Ghalem Belalem ◽  
Fatma Zohra Belkredim
Keyword(s):  
Performance Improvement ◽  
Association Rules ◽  
Query Expansion ◽  
Arabic Language ◽  
Selection Method ◽  
Retrieval Performance ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Significant Performance ◽  
Selection Of

Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an improvement in the performance. One of the main challenges of query expansion is the selection of appropriate terms. In this paper, the authors review this problem using Arabic WordNet and Association Rules within the context of Arabic Language. The results obtained confirmed that with an appropriate selection method, the authors are able to exploit Arabic WordNet to improve the retrieval performance. Their empirical results on a sub-corpus from the Xinhua collection showed that their automatic selection method has achieved a significant performance improvement in terms of MAP and recall and a better precision with the first top retrieved documents.

Mining Undominated Association Rules Through Interestingness Measures

2014 ◽  
Vol 23 (04) ◽  
pp. 1460011 ◽  
Author(s):  
Slim Bouker ◽  
Rabie Saidi ◽  
Sadok Ben Yahia ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Association Rules ◽  
Threshold Value ◽  
Interestingness Measures ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Efficient Selection ◽  
Selection Of

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. In order to bypass this hamper, an efficient selection of rules has to be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures gave rise to a new problem, namely the heterogeneity of the evaluation results and this created confusion to the decision. In this respect, we propose a novel approach to discover interesting association rules without favoring or excluding any measure by adopting the notion of dominance between association rules. Our approach bypasses the problem of measure heterogeneity and unveils a compromise between their evaluations. Interestingly enough, the proposed approach also avoids another non-trivial problem which is the threshold value specification. Extensive carried out experiments on benchmark datasets show the benefits of the introduced approach.

scholarly journals Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments

2012 ◽  
Vol 4 (4) ◽  
pp. 35-64 ◽  
Author(s):  
Mikhail Anufriev ◽  
Cars Hommes
Keyword(s):  
Asset Pricing ◽  
Simple Model ◽  
Predictive Power ◽  
Experimental Setting ◽  
Price Behavior ◽  
Switching Model ◽  
Evolutionary Selection ◽  
Out Of Sample ◽  
Monotonic Convergence ◽  
Selection Of

In recent “learning to forecast” experiments (Hommes et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations, and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea is evolutionary selection among heterogeneous expectation rules, driven by their relative performance. The out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting and aggregate price behavior. (JEL C53, C91, D83, D84, G12)

scholarly journals A Method of Power Supply Mode Selection for Urban Distribution Network Planning Based on Association Rules

2015 ◽  
Vol 3 (5) ◽  
pp. 421-433 ◽  
Author(s):  
Cunbin Li ◽  
Shuke Li ◽  
Yunqi Liu
Keyword(s):  
Association Rules ◽  
Power Supply ◽  
Mode Selection ◽  
Distribution Network ◽  
Network Planning ◽  
Distribution Network Planning ◽  
Urban Distribution ◽  
Supply Mode ◽  
Power Supply Mode ◽  
Selection Of

AbstractBased on association rules, this article proposed a method for intelligent recommendation of power supply mode, which helps decision-makers in the selection of many schemes. Firstly, a history database which includes the forecasting models and correlative factors was first built and association rule mining was conducted; then combined with the correlative factors in the designated area, the criteria matching in the rules mined were carried out with CBR technique; finally automatic recommendation of the power supply modes was achieved under the given conditions. By application of an example, it is demonstrated that the proposed method can not only automatically analyze the applicability of power supply modes and the intrinsic relationship between correlative factors but also provide, to some extent, theoretical basis for selection of power supply modes and practical utility for urban distribution network planning.

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