1241550
Spectrum of Variable-Random Trees
In this paper, we show that a continuous spectrum of randomisation exists, in which most existing tree randomisations are only operating around the two ends of the spectrum. That leaves a huge part of the spectrum largely unexplored. We propose a base learner VR-Tree which generates trees with variable-randomness. VR-Trees are able to span from the conventional deterministic trees to the complete-random trees using a probabilistic parameter. Using VR-Trees as the base models, we explore the entire spectrum of randomised ensembles, together with Bagging and Random Subspace. We discover that the two halves of the spectrum have their distinct characteristics; and the understanding of which allows us to propose a new approach in building better decision tree ensembles. We name this approach Coalescence, which coalesces a number of points in the random-half of the spectrum. Coalescence acts as a committee of "experts" to cater for unforeseeable conditions presented in training data. Coalescence is found to perform better than any single operating point in the spectrum, without the need to tune to a specific level of randomness. In our empirical study, Coalescence ranks top among the benchmarking ensemble methods including Random Forests, Random Subspace and C5 Boosting; and only Coalescence is significantly better than Bagging and Max-Diverse Ensemble among all the methods in the comparison. Although Coalescence is not significantly better than Random Forests, we have identified conditions under which one will perform better than the other.
2550685
Ensembling local learners ThroughMultimodal perturbation
Ensemble learning algorithms train multiple component learners and then combine their predictions. In order to generate a strong ensemble, the component learners should be with high accuracy as well as high diversity. A popularly used scheme in generating accurate but diverse component learners is to perturb the training data with resampling methods, such as the bootstrap sampling used in bagging. However, such a scheme is not very effective on local learners such as nearest-neighbor classifiers because a slight change in training data can hardly result in local learners with big differences. In this paper, a new ensemble algorithm named Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is proposed for building ensembles of local learners, which utilizes multimodal perturbation to help generate accurate but diverse component learners. In detail, FASBIR employs the perturbation on the training data with bootstrap sampling, the perturbation on the input attributes with attribute filtering and attribute subspace selection, and the perturbation on the learning parameters with randomly configured distance metrics. A large empirical study shows that FASBIR is effective in building ensembles of nearest-neighbor classifiers, whose performance is better than that of many other ensemble 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.
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.
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.
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.
14798479
Learning the Semantic Meaning of a Concept from the Web
Many researchers have used text classification method in solving the ontology mapping problem. Their mapping results heavily depend on the availability of quality exemplars used as training data. However, manual preparation of exemplars is costly. In this work, we propose to automatically extract text from web pages returned by a search engine. Search queries are formed according to the semantic information given in the ontology. We have implemented a prototype system that automates the entire process (from search query formation to conditional probability calculation) and conducted a series of experiments. We assessed the effectiveness of our approach by comparing the obtained conditional probabilities with human expectations. Our main contribution is that we explored the possibilities of utilizing web information for text classification based ontology mapping and made several valuable discoveries on its usefulness for future research.
6740690
An integer programming approach for static mapping of paths onto heterogeneous real-time systems
In this paper, we study the problem of mapping a set of independent paths onto a heterogeneous real-time system. Each path is a pipeline of several stages where each stage consists of one application. Each path has latency and throughput requirements that have to be satisfied. The general goal of this problem is to find an initial static mapping of all applications onto available system resources to maximize the allowable increase in input load, until dynamic remapping is required to avoid Quality of Service (QoS) violations on any path. The objective function considered in this paper is to find a static mapping such that the sum of the latencies of all paths is minimized, while satisfying all latency and throughput requirements. The motivation behind it is to give more room for each path to accommodate additional load at run-time. We develop an integer programming (IP) formulation for modeling this mapping problem. By solving the formulation, a feasible mapping can be found that satisfies latency and throughput requirements of all paths and optimizes the objective function. Two heuristics are given along with the IP formulation to provide efficient solutions. Experimental results show the correctness of the IP formulation and the effectiveness of our heuristics.
6854477
Confining windows inter-process communications for OS-level virtual machine
As OS-level virtualization technology usually imposes little overhead on virtual machine start-up and running, it provides an excellent choice for building intrusion/fault tolerant applications that require redundancy and frequent invocation. When developing Windows OS-level virtual machine, however, people will inevitably face the challenge of confining Windows Inter-Process Communications (IPC). As IPC on Windows platform is more complex than UNIX style OS and most of the programs on Windows are not open-source, it is difficult to discover all of the performed IPCs and confine them. In this paper, we propose three general principles to confine IPC on Windows OS and a novel IPC confinement mechanism based on the principles. With the mechanism, for the first time from the literature, we successfully virtualized RPC System Service (RPCSS) and Internet Information Server (IIS) on Feather-weight Virtual Machine (FVM). Experimental results demonstrate that multiple IIS web server instances can simultaneously run on single Windows OS with much less performance overhead than other popular VM technology, offering a good basis for constructing dependable system.
52872567
A Clustering Approach to Planning Base Station and Relay Station Locations in IEEE 802.16j Multi-Hop Relay Networks
In this paper, a clustering approach to solve a network planning problem for 802.16J relay networks is considered. Our clustering approach consists of three basic steps: (1) divide the nodes into k distinct clusters, (2) solve the planning problem separately for each cluster, and (3) perform a final optimization to reduce issues arising at cluster boundaries. Simulation results show that our approach is more efficient than existing approaches: solutions of equivalent quality can be found in 40% of the time. Thus our technique can be used to solve larger problems with similar hardware, or similar size problems in less time.
18554601
Fault-tolerant routing algorithm for RDT structure
RDT (recursive diagonal torus) is a class of interconnection networks for massively parallel computers (MPCs). It consists of recursively structured mesh/torus connections. It has been proved that RDT supports better performances than most direct networks. SINOMP designed for the structure simulation and performance analysis between RDT and those networks is proposed. The simulation results also prove the good performances of RDT. In this paper, the fault-tolerant routing algorithms on the RDT structure are proposed and discussed. They select one of the shortest routes computed with the routing vector on the non-faulty network by using the different rank order. The additional hops are used to send packet only when all the shortest routes have faulty links/nodes. The proposed algorithms need no additional virtual channels for fault tolerant and support a reasonably large number of faults without significantly degrading the system performance.