11987571
Predicting Future Customers via Ensembling Gradually Expanded Trees
Our LAMDAer team has won the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition (open category) grand champion. This report presents our solution to the PAKDD 2006 Data Mining Competition. Following a brief description of the task, we discuss the difficulties of the task and explain the motivation of our solution. Then, we propose the Gradually Expanded Tree Ensemble (GetEnsemble) method, which handles the difficulties via ensembling expanded trees. We evaluated the proposed method and several other methods using AUC, and found the proposed method beats others in this task. Besides, we show how to obtain cues on which kind of second generation (2G) customers are likely to become third generation (3G) users with the proposed method.
16271421
Weak Evolvability Equals Strong Evolvability
A process for the purification of caffeine which comprises carrying out the following steps in any order; (a) recrystallization of caffeine from an aqueous alkaline solution of caffeine containing a reducing agent and (b) extraction of the caffeine from an aqueous alkaline solution of caffeine with a substantially water-immiscible solvent which is liquid under the conditions of the process.
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.
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.
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..
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.
8475658
Energy Minimization for Real-Time Data Gathering in Wireless Sensor Networks
This paper studies the challenging problem of energy minimization for data gathering over a multiple-sources single-sink communication substrate in wireless sensor networks by exploring the energy-latency tradeoffs using rate adaptation techniques. We consider a real-time scenario for mission-critical applications, where the data gathering must be performed within a specified latency constraint. We first propose an offline numerical optimization algorithm with performance analysis for a special case with a complete binary data gathering tree. Then, by discretizing the transmission time, we present a simple, distributed on-line protocol that relies only on the local information available at each sensor node. Extensive simulations were conducted for both long and short-range communication scenarios using two different source placement models. We used the baseline of transmitting all packets at the highest speed and shutting down the radios afterwards. Our simulation results show that compared with this baseline, up to 90% energy savings can be achieved by our techniques (both off-line and on-line), under different settings of several key system parameters
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.
1810758
Display-only file server: a solution against information theft due to insider attack
Insider attack is one of the most serious cybersecurity threats to corporate America. Among all insider threats, information theft is considered the most damaging in terms of potential financial loss. Moreover, it is also especially difficult to detect and prevent, because in many cases the attacker has the proper authority to access the stolen information. According to the 2003 CSI/FBI Computer Crime and Security Survey, theft of proprietary information was the single largest category of losses in the 2003 survey totaling $70.1 million or 35% of the total financial loss reported in that survey. In this paper, we describe the design, implementation and evaluation of an industrial-strength solution called <i>Display-Only File Server</i> (DOFS), which can transparently and effectively stop information theft by insiders in most cases, even if the insiders have proper authorities to read/write the protected information. The DOFS architecture ensures that bits of a protected file never leave a DOFS server after the file is checked in and users can still interact with the protected files in the same way as if it is stored locally. Essentially, DOFS decouples "display access" from other types of accesses to a protected file by providing users only the "display image" rather than the bits of the files, and applies the thin-client computing model on existing client-server applications.
6882751
Applications of a feather-weight virtual machine
A Feather-weight Virtual Machine (FVM) is an OS-level virtualization technology that enables multiple isolated execution environments to exist on a single Windows kernel. The key design goal of FVM is efficient resource sharing among VMs so as to minimize VM startup/shutdown cost and scale to a larger number of concurrent VM instances. As a result, FVM provides an effective platform for fault-tolerant and intrusion-tolerant applications that require frequent invocation and termination of dispensable VMs. This paper presents three complete applications of the FVM technology: scalable web site testing; shared binary service for application deployment and distributed Display-Only File Server (DOFS). To identify malicious web sites that exploit browser vulnerabilities, we use a web crawler to access untrusted sites, render their pages in multiple browsers each running in a separate VM, and monitor their execution behaviors. To allow Windows-based end user machines to share binaries that are stored, managed and patched on a central location, we run shared binaries in a special VM on the end user machine whose runtime environment is imported from the central binary server. To protect confidential files in a file server against information theft by insiders, we ensure that file viewing/editing programs run in a VM, which grants file content display but prevents file content from being saved on the host machine. In this paper, we show how to customize the generic FVM framework to accommodate the needs of the three applications, and present experimental results that demonstrate their performance and effectiveness.
9419066
An Improved Two-Stage Camera Calibration Method Based on Particle Swarm Optimization
According to the calibration of binocular vision, an improved two-stage camera calibration method involved with multi-distortion coefficients is introduced in this paper. At the first stage, the 3D points' coordinate are calculated by the imitated direct linear transformation (DLT) triangulation based on distortion compensation. And at the second stage, particle swarm optimization (PSO) is selected to determine two cameras' parameters. In this way the parameters of the two cameras can be tuned simultaneously. In order to assist estimating the performance of the proposed method, a new cost function is designed. Simulation and experiment are made under the same calibration data sets. The performance of PSO used to tune the parameters is also compared to that of GA. The experiment results show that the strategy of taking the 3D reconstruction errors as object function is feasible and PSO is the best choice for camera parameters' optimization.