Feature interval learning algorithms for classification

Cataloged from PDF version of article.This paper presents Feature Interval Learning algorithms (FIL) which represent multi-concept descriptions in the form of disjoint feature intervals. The FIL algorithms are batch supervised inductive learning algorithms and use feature projections of the training instances to represent induced classification knowledge. The concept description is learned separately for each feature and is in the form of a set of disjoint intervals. The class of an unseen instance is determined by the weighted-majority voting of the feature predictions. The basic FIL algorithm is enhanced with adaptive interval and feature weight schemes in order to handle noisy and irrelevant features. The algorithms are empirically evaluated on twelve data sets from the UCI repository and are compared with k-NN, k-NNFP, and NBC classification algorithms. The experiments demonstrate that the FIL algorithms are robust to irrelevant features and missing feature values, achieve accuracy comparable to the best of the existing algorithms with significantly less average running times. (C) 2010 Elsevier B.V. All rights reserved

Reward-Rate Maximization in Sequential-Identification Under a Stochastic Deadline

Cataloged from PDF version of article.Any intelligent system performing evidence-based decision making under time pressure must negotiate a speed-accuracy trade-off. In computer science and engineering, this is typically modeled as minimizing a Bayes-risk functional that is a linear combination of expected decision delay and expected terminal decision loss. In neuroscience and psychology, however, it is often modeled as maximizing the long-term reward rate, or the ratio of expected terminal reward and expected decision delay. The two approaches have opposing advantages and disadvantages. While Bayes-risk minimization can be solved with powerful dynamic programming techniques unlike reward-rate maximization, it also requires the explicit specification of the relative costs of decision delay and error, which is obviated by reward-rate maximization. Here, we demonstrate that, for a large class of sequential multihypothesis identification problems under a stochastic deadline, the reward-rate maximization is equivalent to a special case of Bayes-risk minimization, in which the optimal policy that attains the minimal risk when the unit sampling cost is exactly the maximal reward rate is also the policy that attains maximal reward rate. We show that the maximum reward rate is the unique unit sampling cost for which the expected total observation cost and expected terminal reward break even under every Bayes-risk optimal decision rule. This interplay between reward-rate maximization and Bayesrisk minimization formulations allows us to show that maximum reward rate is always attained. We can compute the policy that maximizes reward rate by solving an inverse Bayes-risk minimization problem, whereby we know the Bayes risk of the optimal policy and need to find the associated unit sampling cost parameter. Leveraging this equivalence, we derive an iterative dynamic programming procedure for solving the reward-rate maximization problem exponentially fast, thus incorporating the advantages of both the reward-rate maximization and Bayes-risk minimization formulations. As an illustration, we will apply the procedure to a two-hypothesis identification example

American Step-Up and Step-Down Default Swaps under Levy Models

This paper studies the valuation of a class of default swaps with the embedded option to switch to a different premium and notional principal anytime prior to a credit event. These are early exercisable contracts that give the protection buyer or seller the right to step-up, step-down, or cancel the swap position. The pricing problem is formulated under a structural credit risk model based on Levy processes. This leads to the analytic and numerical studies of several optimal stopping problems subject to early termination due to default. In a general spectrally negative Levy model, we rigorously derive the optimal exercise strategy. This allows for instant computation of the credit spread under various specifications. Numerical examples are provided to examine the impacts of default risk and contractual features on the credit spread and exercise strategy.Comment: 35 pages, 5 figure

Learning feature-projection based classifiers

This paper aims at designing better performing feature-projection based classification algorithms and presents two new such algorithms. These algorithms are batch supervised learning algorithms and represent induced classification knowledge as feature intervals. In both algorithms, each feature participates in the classification by giving real-valued votes to classes. The prediction for an unseen example is the class receiving the highest vote. The first algorithm, OFP.MC, learns on each feature pairwise disjoint intervals which minimize feature classification error. The second algorithm, GFP.MC, constructs feature intervals by greedily improving the feature classification error. The new algorithms are empirically evaluated on twenty datasets from the UCI repository and are compared with the existing feature-projection based classification algorithms (FIL.IF, VFI5, CFP, k-NNFP, and NBC). The experiments demonstrate that the OFP.MC algorithm outperforms other feature-projection based classification algorithms. The GFP.MC algorithm is slightly inferior to the OFP.MC algorithm, but, if it is used for datasets with large number of instances, then it reduces the space requirement of the OFP.MC algorithm. The new algorithms are insensitive to boundary noise unlike the other feature-projection based classification algorithms considered here. © 2011 Elsevier Ltd. All rights reserved