Above are papers related to the following paper: [Paper_ID] Paper #140005. 	[Title] Exploiting the Omission of Irrelevant Data [Abstract] Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only irrelevant attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking", and then demonstrate that these omissions can be useful, by proving that certain classes that seem hard to learn in the general PAC model | viz., decision trees and DNF formulae | are trivial to learn in this setting. We also show that this model can be extended to deal with (1) theory revision (i.e., modifying an existing formula); (2) blockers that occasionally include superfluous values or exclude required values; and (3) other cor ruptions of the training data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10186. 	[Title] Learning Default Concepts [Abstract] Classical concepts, based on necessary and sufficient defining conditions, cannot classify logically insufficient object descriptions. Many reasoning systems avoid this limitation by using "default concepts" to classify incompletely described objects. We address the task of learning such default concepts from observational data. We model the underlying performance task | classifying incomplete examples | by a probabilistic process where random test examples are passed through a "blocker" that can hide object attributes from the classifier. We then address the task of learning accurate default concepts from random training examples. We survey the learning techniques that have been proposed for this task in the machine learning and knowledge representation literatures, investigate the relative merits of each, and show that a superior learning technique can be developed from well known statistical principles. Finally, we extend Valiant's pac-learning framework to this context and obtain a number of useful learnability results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5966. 	[Title] Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm [Abstract] This paper introduces ICET, a new algorithm for costsensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for costsensitive classification EG2, CS-ID3, and IDX and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICETs search in bias space and discovers a way to improve the search. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3217. 	[Title] Theory and Applications of Agnostic PAC-Learning with Small Decision Trees [Abstract] We exhibit a theoretically founded algorithm T2 for agnostic PAC-learning of decision trees of at most 2 levels, whose computation time is almost linear in the size of the training set. We evaluate the performance of this learning algorithm T2 on 15 common real-world datasets, and show that for most of these datasets T2 provides simple decision trees with little or no loss in predictive power (compared with C4.5). In fact, for datasets with continuous attributes its error rate tends to be lower than that of C4.5. To the best of our knowledge this is the first time that a PAC-learning algorithm is shown to be applicable to real-world classification problems. Since one can prove that T2 is an agnostic PAC-learning algorithm, T2 is guaranteed to produce close to optimal 2-level decision trees from sufficiently large training sets for any (!) distribution of data. In this regard T2 differs strongly from all other learning algorithms that are considered in applied machine learning, for which no guarantee can be given about their performance on new datasets. We also demonstrate that this algorithm T2 can be used as a diagnostic tool for the investigation of the expressive limits of 2-level decision trees. Finally, T2, in combination with new bounds on the VC-dimension of decision trees of bounded depth that we derive, provides us now for the first time with the tools necessary for comparing learning curves of decision trees for real-world datasets with the theoretical estimates of PAC learning theory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #86840. 	[Title] Learning Active Classifiers [Abstract] Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can | at some cost | obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework | which is perhaps the main contribution of this paper | we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18781. 	[Title] Learning to Reason with a Restricted View [Abstract] The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge. Computational considerations show that this is a useful paradigm; in some cases learning and reasoning problems that are intractable when studied separately become tractable when performed as a task of Learning to Reason. In this paper we study Learning to Reason problems where the interaction with the world supplies the learner only partial information in the form of partial assignments. Several natural interpretations of partial assignments are considered and learning and reasoning algorithms using these are developed. The results presented exhibit a tradeoff between learnability, the strength of the oracles used in the interface, and the range of reasoning queries the learner is guaranteed to answer correctly. Please summarise all the given information.

Paper #140005 has the title `Exploiting the Omission of Irrelevant Data  
` and the abstract `Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only irrelevant attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking", and then demonstrate that these omissions can be useful, by proving that certain classes that seem hard to learn in the general PAC model | viz., decision trees and DNF formulae | are trivial to learn in this setting. We also show that this model can be extended to deal with (1) theory revision (i.e., modifying an existing formula); (2) blockers that occasionally include superfluous values or exclude required values; and (3) other cor ruptions of the training data. 
`.
Question: Which category should Paper #140005 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #140005 has the title `Exploiting the Omission of Irrelevant Data  
` and the abstract `Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only irrelevant attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking", and then demonstrate that these omissions can be useful, by proving that certain classes that seem hard to learn in the general PAC model | viz., decision trees and DNF formulae | are trivial to learn in this setting. We also show that this model can be extended to deal with (1) theory revision (i.e., modifying an existing formula); (2) blockers that occasionally include superfluous values or exclude required values; and (3) other cor ruptions of the training data. 
`.
Question: Which category should Paper #140005 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Theory