A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework

This paper develops a new algorithm for inducing cost-sensitive decision trees that is inspired by the multi-armed bandit problem, in which a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to pay out the most. Game Theory proposes a solution to this multi-armed bandit problem by using a process of exploration and exploitation in which reward is maximized. This paper utilizes these concepts to develop a new algorithm by viewing the rewards as a reduction in costs, and utilizing the exploration and exploitation techniques so that a compromise between decisions based on accuracy and decisions based on costs can be found. The algorithm employs the notion of lever pulls in the multi-armed bandit game to select the attributes during decision tree induction, using a look-ahead methodology to explore potential attributes and exploit the attributes which maximizes the reward. The new algorithm is evaluated on fifteen datasets and compared to six well-known algorithms J48, EG2, MetaCost, AdaCostM1, ICET and ACT. The results obtained show that the new multi-armed based algorithm can produce more cost-effective trees without compromising accuracy. The paper also includes a critical appraisal of the limitations of the new algorithm and proposes avenues for further research

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

Conscription and critique

This article focuses on the discussion of general conscription in Walter Benjamin’s 1921 essay “Toward the Critique of Violence.” In the essay, Benjamin presents conscription or compulsory military service alongside his discussions of police violence and capital punishment, and as one form of legal violence in which law-preserving and law-positing violence coincide and mix. This article proposes that Benjamin’s discussion of conscription should be read as a formal model for understanding how legal subjectification in the modern state works more generally, and how such subjectification circumscribes critique. This reading is offered through a series of snapshots of various veins and elements in Benjamin’s essay, while also connecting this interpretation to the work of a number of contemporary scholars of postcoloniality, namely, Talal Asad, David Scott and Samera Esmeir, who all invoke conscription as a particularly powerful metaphor for describing modern law’s tendency to colonize critique

Water and power: Reintegrating the state into the study of Egyptian irrigation

The study of irrigation in ancient Egypt has swung between two poles. Early environmental‐determinist scholarship stressed the imperative of state control while the most recent work denies the state any significant role and instead emphasizes the agency of local communities. This article briefly explores the historiography of Egyptian irrigation, critiquing both its colonialist roots and the extreme reaction against colonialist preconceptions that marks current scholarship. A case study of Roman state coordination is then presented as an argument for reintegrating the state into the history of Egyptian water management.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138919/1/hic312394.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138919/2/hic312394_am.pd

Optimal constraint-based decision tree induction from itemset lattices

International audienceIn this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction