text: Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose a general framework and method that overcomes these and other limitations. The formulation of our method uses any differentiable classification function. We demonstrate the method by using Gaussian kernel SVMs. We constrain the inverse classification to occur on features that can actually be changed, each of which incurs an individual cost. We further subject such changes to fall within a certain level of cumulative change (budget). Our framework can also accommodate the estimation of features whose values change as a consequence of actions taken (indirectly changeable features). Furthermore, we propose two methods for specifying feature-value ranges that result in different algorithmic behavior. We apply our method, and a proposed sensitivity analysis-based benchmark method, to two freely available datasets: Student Performance, from the UCI Machine Learning Repository and a real-world cardiovascular disease dataset. The results obtained demonstrate the validity and benefits of our framework and method.
  @cite_15: An important issue of data mining is how to transfer data into information, the information into action, and the action into value or profit. This paper presents a study on applying sensitivity analysis to neural network models for a particular area in data mining, interesting mining and profit mining. Applying sensitivity analysis to neural network models rather than just regression models can help us identify sensible factors that play important roles to dependent variables such as total profit in a dynamic environment.
  @cite_22: A method and apparatus for filling a treatment vessel, such as a digestor, with fiber material and liquid. A chips-liquid mixture from a pretreatment vessel is fed through a conventional feeding valve to a treatment vessel while the chips-liquid mixture is being acted on by other means in the feeding valve. The feeding valve includes a rotor having a number of pockets extending therethrough, and a plurality of inlets and outlets arranged around the periphery of the valve for communication with the pockets thereof. In a first position of the valve chips-liquid mixture is fed into a pocket, and a portion of the liquid is strained off, then the valve rotates to a second position wherein a quantity of the liquid is displaced by gas or steam while the chips are maintained in the pocket, and then the valve rotates to a third position wherein the chips in the pocket are discharged into the treatment vessel. After discharge of the chips into the treatment vessel the valve rotates to a fourth position wherein the steam in the pocket is discharged to a pretreatment vessel and the pocket is filled with liquid so that it is filled with liquid when it is rotated back to its first position.
summary: Inverse classification can be seen as a form of sensitivity analysis, the process of examining the input features' effects on the target output. While there are many forms of sensitivity analysis @cite_22 @cite_15 , inverse classification is most similar to local sensitivity analysis and variable perturbation method. Later on (Section III), we propose a benchmark method that is based on these.