0 ≤ α ≤ 1 is a parameter for approximating KL divergence D. The score can be recast into a similarity score via, for example, the following function (Fujita and Sato, 2008): This measure offers an advantage: the weight for each feature is determined theoretically.
the optimization of α is difficult because it varies according to the task and even the data size (confidence of probability distributions).