Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches.
Action rules (or actionable patterns) describe possible transitions of objects from one state to another with respect to a distinguished attribute. Strategies for discovering them can be divided into two types: rule based and object based. Rule-based actionable patterns are built on the foundations of preexisting rules. This approach consists of two main steps: (1) a standard learning method is used to detect interesting patterns in the form of classification rules, association rules, or clusters; (2) the second step is to use an automatic or semiautomatic strategy to inspect such results and derive possible action strategies. These strategies provide an insight of how values of some attributes need to be changed so the desirable objects can be shifted to a desirable group. Object-based approach assumes that actionable patterns are extracted directly from a database. System DEAR, presented in this paper, is an example of a rule-based approach. System ARD and system for association rules mining are examples of an object-based approach. Music Information Retrieval (MIR) is taken as an application domain. We show how to manipulate the music score using action rules. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc. In this paper, we present an algorithm that discovers action rules from a decision table. Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. The previous research on action rule discovery required the extraction of classification rules before constructing any action rule. The new proposed algorithm does not require pre-existing classification rules, and it uses a bottom up approach to generate action rules having minimal attribute involvement.  Decision tables classifying customers into groups of different profitability are used for mining rules classifying customers. Attributes are divided into two groups: stable and flexible. By stable attributes we mean attributes which values can not be changed by a bank (age, marital status, number of children are the examples). On the other hand attributes (like percentage rate or loan approval to buy a house in certain area) which values can be changed or influenced by a bank are called flexible. Rules are extracted from a decision table given preference to flexible attributes. This new class of rules forms a special repository of rules from which new rules called actionrules are constructed. They show what actions should be taken to improve the profitability of customers.