1988413
Test strategies for cost-sensitive decision trees
In medical diagnosis, doctors must often determine what medical tests (e.g., X-ray and blood tests) should be ordered for a patient to minimize the total cost of medical tests and misdiagnosis. In this paper, we design cost-sensitive machine learning algorithms to model this learning and diagnosis process. Medical tests are like attributes in machine learning whose values may be obtained at a cost (attribute cost), and misdiagnoses are like misclassifications which may also incur a cost (misclassification cost). We first propose a lazy decision tree learning algorithm that minimizes the sum of attribute costs and misclassification costs. Then, we design several novel "test strategies" that can request to obtain values of unknown attributes at a cost (similar to doctors' ordering of medical tests at a cost) in order to minimize the total cost for test examples (new patients). These test strategies correspond to different situations in real-world diagnoses. We empirically evaluate these test strategies, and show that they are effective and outperform previous methods. Our results can be readily applied to real-world diagnosis tasks. A case study on heart disease is given throughout the paper
11767978
Real-Time Scheduling for Multi-agent Call Center Automation
In a call center, service agents with different capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents efficiently. We developed SANet, a service agent network for call center, which integrates multiple service agents including both software agents and human agents, and employs a broker to schedule customer problems to service agents for better solutions according to their changing capabilities and availability. This paper describes the real-time scheduling method in SANet as well as its architecture. There are two phases in our scheduling method. One is problem-type learning. The broker is trained to learn the problem types and hence can decide the type of incoming problems automatically. The other is the scheduling algorithm based on problem types, capabilities and availability of service agents. We highlight an application in which we apply SANet to a call center problem for a cable-TV company. Finally, we support our claims via experimental results and discuss related works.
13595177
Multiple-Goal Recognition from Low-Level Signals
Researchers and practitioners from both the artificial intelligence and pervasive computing communities have been paying increasing attention to the task of inferring users' high-level goals from low-level sensor readings. A common assumption made by most approaches is that a user either has a single goal in mind, or achieves several goals sequentially. However, in real-world environments, a user often has multiple goals that are concurrently carried out, and a single action can serve as a common step towards multiple goals. In this paper, we formulate the multiple-goal recognition problem and exemplify it in an indoor environment where an RF-based wireless network is available. We propose a goal-recognition algorithm based on a dynamic model set and show how goal models evolve over time based on pre-defined states. Experiments with real data demonstrate that our method can accurately and efficiently recognize multiple interleaving goals in a user's trace.
9645876
Handling feature interactions in process-planning
Abstract The development of a successful approach to the integration of computer-aided design and computer-aided process-planning will require solutions to several fundamental problems in planning and geometric reasoning. In this article, we consider two such problems, both of which arise from interactions among machinable features. The first problem is that interactions can lead to several alternative interpretations of what the machinable features are. We address this problem by developing an algebra of features. The second problem is that interactions among features can cause problems in optimizing the process plans. For this problem, we have developed a branch-and-bound optimization algorithm with good heuristics to guide the search. This article presents the theory and mathematical framework for both problems and also the details of the implementation and experimental results.
15099164
A scalable supervised algorithm for dimensionality reduction on streaming data
Algorithms on streaming data have attracted increasing attention in the past decade. Among them, dimensionality reduction algorithms are greatly interesting due to the desirability of real tasks. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction approaches. However, PCA is not optimal for general classification problems because it is unsupervised and ignores valuable label information for classification. On the other hand, the performance of LDA is degraded when encountering limited available low-dimensional spaces and singularity problem. Recently, Maximum Margin Criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, the original MMC algorithm could not satisfy the streaming data model to handle large-scale high-dimensional data set. Thus an effective, efficient and scalable approach is needed. In this paper, we propose a supervised incremental dimensionality reduction algorithm and its extension to infer adaptive low-dimensional spaces by optimizing the maximum margin criterion. Experimental results on a synthetic dataset and real datasets demonstrate the superior performance of our proposed algorithm on streaming data.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
32009068
Finding biclusters by random projections
Given a matrix X composed of symbols, a bicluster is a submatrix of X obtained by removing some of the rows and some of the columns of X in such a way that each row of what is left reads the same string. In this paper, we are concerned with the problem of finding the bicluster with the largest area in a large matrix X. The problem is first proved to be NP-complete. We present a fast and efficient randomized algorithm that discovers the largest bicluster by random projections. A detailed probabilistic analysis of the algorithm and an asymptotic study of the statistical significance of the solutions are given. We report results of extensive simulations on synthetic data.
11280884
A novel cache design for vector processing
This paper introduces an innovative cache design for vector computers, called prime-mapped cache. By utilizing the special properties of a Mersenne prime, the new design does not increase the critical path length of a processor, nor does it increase the cache access time as compared to a direct-mapped cache. The prime-mapped cache minimizes cache miss ratio caused by line interferences that have been shown to be critical for numerical applications by previous investigators. We show that significant performance gains are possible by adding the proposed cache memory into an existing vector computer provided that application programs can be blocked. The performance gain will increase with the increase of the speed gap between processors and memories. We develop an analytical performance model based on a generic vector computation model to study the performance of the design. Our preliminary performance analysis on various vector access patterns shows that the prime-mapped cache can provide as much as a factor of 2 to 3 performance improvement over the conventional direct-mapped cache in the vector processing environment. Moreover, the additional hardware cost introduced by the new mapping scheme is negligible.
9377268
Introducing SCSI-to-IP cache for storage area networks
Data storage plays an essential role in today's fast-growing data-intensive network services. iSCSI is one of the most recent standards that allow SCSI protocols to be carried out over IP networks. However, the disparities between SCSI and IP prevent fast and efficient deployment of SAN (storage area network) over IP. This paper introduces STICS (SCSI-To-IP cache storage), a novel storage architecture that couples reliable and high-speed data caching with low-overhead conversion between SCSI and IP protocols. Through the efficient caching algorithm and localization of certain unnecessary protocol overheads, STICS significantly improves performance over current iSCSI system. Furthermore, STICS can be used as a basic plug-and-play building block for data storage over IP. We have implemented software STICS prototype on Linux operating system. Numerical results using popular PostMark benchmark program and EMC's trace have shown dramatic performance gain over the current iSCSI implementation.
20437986
Design and analysis of multiple-bus arbiters with different priority schemes
The authors present four multiple-user and multiple-server arbiter designs of multiple-bus multiprocessors. All designs are based on decentralized control, which has the advantage of good reliability and potentially high throughput. The priority protocols considered are fixed priority, rotating priority, round-robin priority, and equal priority. For the first two priority protocols, lookahead techniques are used to speed up the arbitration process. The round-robin protocol is based on the decentralized daisy-chain idea and is operated in an asynchronous fashion. Analytical models are developed for the four different priority protocols to evaluate the performance of the different designs. The analyses are based on a probabilistic model for the synchronous case and simple queuing analysis for the asynchronous system. Numerical results obtained from the proposed model show that the equal priority protocol performs better than the others in the system with a few buses. The performance difference is not significant when the number of buses is relatively large.<<ETX>>
31570297
Performance of Multiple-Bus Interconnections for Multiprocessors
Abstract Bus structures, in general, are easily understood and therefore preferred by manufactures for implementation. Multiple-bus systems can be viewed as an incremental expansion of single-bus architectures that can provide high bandwidth and reliability. This research note provides a brief overview of various analytical techniques suitable for performance evaluation of the multiple-bus multiprocessors. Some results and comparisons based on these techniques are also provided.