8259100
Adapt Bagging to Nearest Neighbor Classifiers
It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.
5420099
A new approach to estimating the expected first hitting time of evolutionary algorithms
The expected first hitting time is an important issue in theoretical analyses of evolutionary algorithms since it implies the average computational time complexity. In this paper, by exploiting the relationship between the convergence rate and the expected first hitting time, a new approach to estimating the expected first hitting time is proposed. This approach is then applied to four evolutionary algorithms which involve operators of mutation, mutation with population, mutation with recombination, and time-variant mutation, respectively. The results show that the proposed approach is helpful for analyzing a broad range of evolutionary algorithms.
14236359
Semi-naive Exploitation of One-Dependence Estimators
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies, one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies whilst keeping the effectiveness of probability estimation, and therefore leading to excellent performance. In previous studies, however, ODEs were exploited in simple ways, such as by averaging, for classification. In this paper, we propose a semi-naive exploitation of ODEs that fits a function of ODEs to pursue higher-order attribute dependencies. Extensive experiments show that the proposed SNODE approach can achieve better performance than many state-of-the-art Bayesian classifiers.
15348979
Cocktail Ensemble for Regression
This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formally analyze the optimal linear combination of two base ensembles, which is then extended to multiple individual ensembles via pairwise combinations. The Cocktail ensemble approach is proposed based on this analysis. Experiments over a broad range of data sets show that the proposed approach outperforms the individual ensembles, two other methods of ensemble combination, and two state-of-the-art regression approaches.
15248817
TEFE: A Time-Efficient Approach to Feature Extraction
With the rapid evolution of Internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One obstacle to high-speed testing is the time cost of feature extraction for test objects, particularly for objects with complex representation such as images, videos and audios. In this paper, we study the problem of reducing test time cost by extracting cheap but sufficient features. We propose the TEFE (time-efficient feature extraction) approach, which balances between the test accuracy and test time cost by extracting a proper subset of features for each test object. In the implementation, TEFE trains a sequence of support vector machines and classifies each test object cascadingly. Empirical study shows that TEFE is time efficient while holding a classification accuracy close to that of using all features. It also shows that the test time is linearly adjustable in TEFE.
33025668
The Permanent Rank of a Matrix
Define the perrank of a matrixAto be the size of the largest square submatrix ofAwith nonzero permanent. Motivated in part by the Alon?Jaeger?Tarsi Conjecture [3], we prove several results on perranks..
14448098
Research on Dynamic Task Assignment in Grid Workflow System
Task assignment in grid workflow system should be designed to adapt well with the grid which allow people from different organizations to work together in the same workflow process. To ensure grid workflow systems could assign the task to the most suitable person, a novel solution is proposed firstly. Then two description languages GHRSL and HRRL are defined. GHRSL (grid human resource specification language) describes people as grid resources. HRRL (human resources requirement language) describes human resources requirement of workflow tasks. Finally, the evaluation method and the algorithm of dynamic task assignment are also proposed. Based on these, the grid workflow system could assign tasks dynamically to the correct person(s)
2701730
Data Gathering with Tunable Compression in Sensor Networks
We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computationally intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective trade-offs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology, as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constant-factor approximation for the grid topology and good average performance on the general graphs. Although, theoretically, a more complicated randomized algorithm offers a polylogarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.
15352374
Network-Aware State Update For Large Scale Mobile Games
In large scale mobile gaming environments, efficient mechanisms for state update is crucial to allow graceful real-time interaction of a large number of players, under limited wireless network bandwidth. By using the state updating threshold as a key parameter that bridges the resulting state inconsistency (or distortion) and the network traffic, we are able to model the fundamental traffic-distortion tradeoffs. Given the tradeoffs for all users, an optimal bandwidth allocation can be derived using well-known convex programming techniques. By exploring the temporal locality of gaming behavior, we also propose a prediction method to realize on-line bandwidth adaptation. Using real data trace from a multi-player driving game, the proposed network aware bandwidth allocation method, NABA, is able to achieve significant reduction in state distortion compared to two baselines: Uniform and Proportional policies. NABA is also adaptable to variations in system parameters, including bandwidth constraint and network delay.
2088482
Cotton Variety Selection Model and Application Design
Choosing a  cotton variety  involves  making comparisons over many trials and across an increasingly large  number of available  varieties. To aid in this process, a computerized tool has been developed at the Texas Tech University. This tool, called COTWIZ, based on the cotton variety selection model, allows  multiple comparisons  of varieties across a number of trial locations. COTWIZ based on the cotton variety selection model and uses cotton OVT data to make  comparisons that are imported by the user.
9006284
A workflow model based on fuzzy-timing Petri nets
Time information management in workflow has been recognized as one of the most significant tasks in workflow management. The uncertainties in time and time-related constraints in workflow models should be taken into consideration. Based on introducing time constraints on elements in fuzzy-timing Petri nets, this paper propose a new workflow model named fuzzy temporal workflow nets (FTWF-nets). The calculation of temporal elements in FTWF-nets is given. Then time modeling and time possibility analysis of temporal phenomena in FTWF-nets are investigated. Finally an example is given to illustrate how to use these methods. Research results show that FTWF-nets can be used to model time information in those workflows, which have time uncertainty and have time constraints on resources and activities, and analyze the time possibility on some constraints.