但是属性约简是一个NP问题，对属性的约简和决策规则的约简只能通过启发式算法实现。
But the attribute reduction is a NP problem, the attribution reduction and decision rule reduction will be solved by method of elicitation.
提出了一种基于条件信息熵的知识约简启发式算法，并指出该算法的时间复杂度是多项式的。
A heuristic algorithm based on conditional information entropy for knowledge reduction is proposed, and the complexity of this algorithm is analyzed.
提出了一种基于条件粒度熵的属性约简的启发式算法，通过例子分析，表明该算法是有效的。
The paper offer a new heuristic attribute reduction algorithm based on conditional granularity entropy, though running an example, we show that this algorithm is effective.
解决这类问题的一般方法是采用启发式算法求出最优或次最优约简。
The common method to solve this problem is to adopt the heuristic algorithm.
为了获得简明的规则集，通常希望能找出最小的属性约简集，而求解最小约简是NP难问题，解决此类难题通常采用启发式算法以求得近似最优解。
The minimum attributes reduction set is expected to acquire the brief regulated set. This is taken as NP-hard Problem, which can be figured out through the heuristic algorithm.
再次，对有序决策表进行了研究，提出了一种基于优势矩阵的启发式属性约简算法。
After that we study on the ordered decision table and propose a new heuristic attribute reduction algorithm based on dominance matrix, whose time complexity is polynomial.
最优属性约简是NP困难问题，目前出现的启发式算法多是以决策表的核为起点。
To consistent decision tables, the minimal reduct has been proved to be NP-hard.
给出了分配约简的一种启发式算法：条件信息量约简算法，分析了该算法的时间复杂度。
Based on conditional information quantity, a heuristic algorithm for assignment reduction is presented, and the complexity of this algorithm is analyzed.
论文以决策强度、条件向量的覆盖度和属性的重要性为启发式信息，提出了条件向量约简的一种启发式算法，通过实验验证了该算法是有效的。
In this paper, a heuristic algorithm based on decision strength, the coverage degree of condition vectors and the significance of attribute for condition vectors' reduction is proposed. Experi...
论文以决策强度、条件向量的覆盖度和属性的重要性为启发式信息，提出了条件向量约简的一种启发式算法，通过实验验证了该算法是有效的。
In this paper, a heuristic algorithm based on decision strength, the coverage degree of condition vectors and the significance of attribute for condition vectors' reduction is proposed. Experi...
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