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Rule discovery is an important classification method that has been attracting a significant amount of researchers in recent years. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category. Besides the classical approaches, many rule mining approaches use biologically-inspired algorithms such as evolutionary algorithms and swarm intelligence approaches. In this paper,...
As acquisition technology progresses, remote sensing data contains an ever increasing amount of information. Future projects in remote sensing will give high repeatability of acquisition like Venμs (CNES1) which may provide data every 2 days with a resolution of 5.3 meters on 12 bands (420nm–900nm) and Sentinel−2 (ESA) 13 bands, 10–60m resolution and 5 days. With such data, process automation appears...
Bandyopadhyay and Pal proposed an improved genetic search strategy, GACD (Genetic Algorithm with Chromosome Differentiation), involving partitioning the chromosomes into two classes, and defining a restricted form of the crossover operator between the two classes. The GACD can be applied to many multi-dimensional pattern recognition problems. However, their GACD suffered from three problems, i.e....
Since Volker Strassen proposed a recursive matrix multiplication algorithm reducing the time complexity to n2.81 in 1968, many scholars have done a lot of research on this basis. In recent years, researchers have proposed using computer algorithms to solve fast matrix multiplication problem. They have found Strassen's algorithm or other algorithms that have the same time complexity as Strassen algorithm...
The algorithm of GASEN (Genetic Algorithm based Selective Ensemble Network) has been proven to be a very effective way to select a subset of neural networks to form an ensemble classifier or a regressor of enhanced generation ability. And yet performance of GASEN on class-imbalance data sets hasn't been discussed widely, while class-imbalance learning itself is an increasingly important issue. In...
One of the most recent technique to design an efficient ATR system is to use high-resolution radar (HRR) imagery as input information flow. To increase the quality of such system, an interesting approach is to use powerful artificial neural networks inside of its recognition chain. Consequently, an improved neural recognition function based on modified feature extraction and selection methods and...
The clustering ensemble is a new topic in machine learning. It can combine multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms have been known as methods with high ability to find the solution of optimization problems like the clustering ensemble problem. So far, many contributions have been done to find consensus cluster partition...
Traditional Learning Classifier Systems (LCS) learn syntactically simple string rules in an auction based competitive market economy by continuously interacting with their environment through a reinforcement program. All classifiers participating in an auction issue a bid proportional to their strength and a winner classifier is allowed to fire and receive a reward or punishment from its environment...
Aiming to the shortages of fuzzy c-means clustering applied to pattern recognition, an improved method by genetic algorithm is proposed. This method can not only automatically optimizes the classification number, but also search the global optimal solution for the clustering center. The experimental results demonstrate this proposed method is excellent for pattern recognition.
In this paper rules adaptive control system based on genetic study classification algorithm is put forward in order to solve the traffic signal's separated, complex, non-linear features and prevent failure of the control rules in urban regional coordination traffic signal control system. In this study, we build the self-organization model by fluid dynamics, and get the control parameters and environment...
Parameter tuning can be a lengthy and exhaustive process. Furthermore, optimal parameter sets are usually not only problem specific, but also problem instance specific. Adaptive genetic algorithms perform parameter control during the run, thus increasing algorithm performance. These mechanisms may also enable the algorithm to escape local optima more efficiently. In this paper, we describe the fitness...
In this paper we propose a clustering ensemble algorithm based on genetic algorithm. The most important feature of our method is ability to extract the number of clusters. Genetic algorithms have been known as methods with high ability to find the solution of optimization problems. One of these problems is clustering, a process that receives a dataset as input and divides its members into several...
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