Journal ArticleDOI
Accuracy-based learning classification system
TLDR
Results demonstrate that the proposed genetic approach provides marked improvement in a number of cases and has been compared with UCS (GA-based classification system) and C4.5 (non GA-based rule induction algorithm).Abstract:
In order to implement a multi-category classification system, an efficient rule set is imperative for its investigation. In this paper, such a system is being introduced. In the first phase of its kind, the C4.5 rule induction algorithm is adopted to obtain useful rule set from classification problem, following a new data set partitioning approach. Next, the presented genetic algorithm (GA) is implemented to refine the learned rules in more efficient way. The resultant system has been compared with UCS (GA-based classification system) and C4.5 (non GA-based rule induction algorithm) on a number of benchmark data sets collected from UCI (University of California at Irvine) machine learning repository. Results demonstrate that the proposed genetic approach provides marked improvement in a number of cases.read more
Citations
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Journal ArticleDOI
A novel nature inspired firefly algorithm with higher order neural network: Performance analysis
TL;DR: A Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units.
Journal ArticleDOI
A genetic algorithm-based rule extraction system
TL;DR: An accuracy-based learning system called DTGA (decision tree and genetic algorithm) that aims to improve prediction accuracy over any classification problem irrespective to domain, size, dimensionality and class distribution is introduced.
Journal ArticleDOI
A self adaptive harmony search based functional link higher order ANN for non-linear data classification
TL;DR: A novel approach of hybridization of higher order neural network (Functional link higher order artificial neural network) with self adaptive harmony search (SAHS) based gradient descent learning (GDL) for non-linear data classification problem.
Journal ArticleDOI
A Benchmark to Select Data Mining Based Classification Algorithms For Business Intelligence And Decision Support Systems
TL;DR: Comparing various classification algorithms that have been frequently used in data mining for decision support systems shows that GA based algorithm is more powerful algorithm and shall be the first choice of organizations for their decision support system.
Journal ArticleDOI
A Global-best Harmony Search based Gradient Descent Learning FLANN (GbHS-GDL-FLANN) for data classification
TL;DR: A variant of Harmony Search, called Global-best Harmony Search along with Gradient Descent Learning is used with Functional Link Artificial Neural Network for classification task in data mining and results reveal that the performance of the proposed GbHS-GDL-FLANN is better and statistically significant from other alternatives.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
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TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.