Cooperative Learning to Minimal Time Cost Attribute Reduction Through Ant Colony Optimization

2015 ◽  
Vol 12 (10) ◽  
pp. 4047-4057
Author(s):  
Ji Dong
2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zilong Xu ◽  
Hong Zhao ◽  
Fan Min ◽  
William Zhu

Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion stage, and the filtration stage. In the addition stage, each ant starts from the initial position and traverses edges probabilistically until the stopping criterion is satisfied. The pheromone of the traveled path is also updated in this process. In the deletion stage, each ant deletes redundant attributes. Two strategies, called the centralized deletion strategy and the distributed deletion strategy, are proposed. Finally, the ant with minimal test cost is selected to construct the reduct in the filtration stage. Experimental results on UCI datasets indicate that the algorithm is significantly better than the information gain-based one. It also outperforms the genetic algorithm on medium-sized dataset Mushroom.


2021 ◽  
Vol 15 ◽  
pp. 68-77
Author(s):  
Alejandro Fuentes-Penna ◽  
Jorge A. Ruiz-Vanoye ◽  
Marcos S. González-Ramírez

The main target of Traveling Salesman Problem (TSP) is to construct the path with the lowest time between different cities, visiting every one once. The Scheduling Project Ant Colony Optimization (SPANCO) Algorithm proposes a way to solve TSP problems adding three aspects: time, cost effort and scope, where the scope is the number of cities, the effort is calculated multiplying time, distance and delivering weight factors and dividing by the sum of them and optimizing the best way to visit the cities graph.


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