A general feature-weighting function for classification problems

•The salience of a feature can be changed given different queries.•Multi-modal problems need a flexible weighting function to deal with modality.•Proposing a differentiable, flexible, and general weighting function.•The proposed is optimized for k-NN algorithm and assessed through experiments.•The experiment results confirm the effectiveness of proposed weighting function. Feature weighting is a vital step in machine learning tasks that is used to approximate the optimal degree of influence of individual features. Because the salience of a feature can be changed by different queries, the majority of existing methods are not sensitive enough to describe the effectiveness of features. We suggest dynamic weights, which are dynamically sensitive to the effectiveness of features. In order to achieve this, we propose a differentiable feature weighting function that dynamically assigns proper weights for each feature, based on the distinct feature values of the query and the instance. The proposed weighting function, which is an extension of our previous work, is suitable for both single-modal and multi-modal weighting problems, and, hence, is referred to as a General Weighting Function. The number of parameters of the proposed weighting function is fewer compared to the ordinary weighting methods. To show the performance of the General Weighting Function, we proposed a classification algorithm based on the notion of dynamic weights, which is optimized for one nearest neighbor algorithm. The experimental results show that the proposed method outperforms the ordinary feature weighting methods.