Minimal test cost attribute reduction is an important problem in cost-sensitive learning since it reduces the dimensionality of the attributes space. To address this issue, many heuristic algorithms have been used by researchers, however, the effectiveness of these algorithms are often unsatisfactory on large-scale datasets. In this paper, we develop a logarithmic weighted algorithm to tackle the minimal test cost attribute reduction problem. More specifically, two major issues are addressed with regard to the logarithmic weighted algorithm. One relates to a logarithmic strategy that can suggest a way of obtaining the attribute reduction to achieve the best results at the lowest cost. The other relates to the test costs which are normalized to speed up the convergence of the algorithm. Experimental results show that our algorithm attains better cost-minimization performance than the existing a weighted information gain algorithm. Moreover, when the test cost distribution is Normal, the effectiveness of the proposed algorithm is more effective for dealing with relatively medium-sized datasets and large-scale datasets.
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- A Logarithmic Weighted Algorithm for Minimal Test Cost Attribute Reduction