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.
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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