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After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network...
Many applications such as pattern recognition require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant or redundant features while keeping the most informative ones. In this paper, an ant colony system approach for solving feature selection for classification is presented. The proposed algorithm was tested...
We give sub linear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sub linear-time algorithms were not known before. These new algorithms use a combination...
The paper addresses the feature selection based on Neighborhood Rough Set (NRS) used as evaluation function and Ant Colony Optimization (ACO) as generation procedure. A NRS-based measure is employed as heuristic information of ACO. For the weakness of setting a specified value to the size of neighborhood, a new standard deviation based value is advanced to be the size of neighborhood. Four datasets...
This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results. At...
Bagging (Bootstrap Aggregating) has been proved to be a useful, effective and simple ensemble learning methodology. In generic bagging methods, all the classifiers which are trained on the different training datasets created by bootstrap resampling original datasets would be seen as base classifiers and their results would be combined to compute final result. This paper proposed a novel ensemble model...
Microarray gene expression data have been used in cancer discovery and prediction characterized by their small samples and large dimensionality. This paper proposes a hybrid method based on improved Ant Colony Optimization (ACO) and Random Forests (RF) for selecting a small set of marker genes from microarray data to produce high accuracy cancer classifier. The method preselects top-ranked features...
So far most of the K-means algorithms use the number of the labeled data as the K value, but sometimes it doesnpsilat work well. In this paper, we propose a semi-supervised K-means algorithm based on the global optimization. It can select an appropriate number of clusters as the K value directly and plan a great amount of supervision data by using only a small amount of the labeled data. Combining...
Research on the technology of classification the rules of current data mining. Because of accuracy and validity of the classification rules meet, this paper brings up a new algorithm for classification rules: classification rule based on backtracking ant colony algorithm. The core of the algorithm is: the project ant crawl forward, at the same time the backtracking ants crawl backward. If the backtracking...
Support vector machine has good generality. Its development for function regressing is not as same as that with fast speed for sample separated. Sequence minimum optimizing (SMO) is effective on large samples, and is used to handle the problems with sparse solutions. Considering the power of rough set (RS) for handling imprecise data, the datum boundary sought by RS will substitute original inputs...
In this paper, a new optimization method, Organizational Evolutionary Algorithm (OEA), is proposed, in which a population is made of organizations and whose evolution is led by three organizational evolutionary operators, i.e. the splitting operator, the merging operator and the cooperating operator; the splitting operator controls the size of organizations and make part of organizations enter into...
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