2919977
High-Order Pattern Discovery from Discrete-Valued Data
To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. The authors propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper.
2310778
Boosting an associative classifier
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.
15440093
Pattern discovery: a data driven approach to decision support
Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.
23887431
Generative and Discriminative Learning by CL-Net
This correspondence presents a two-stage classification learning algorithm. The first stage approximates the class-conditional distribution of a discrete space using a separate mixture model, and the second stage investigates the class posterior probabilities by training a network. The first stage explores the generative information that is inherent in each class by using the Chow-Liu (CL) method, which approximates high-dimensional probability with a tree structure, namely, a dependence tree, whereas the second stage concentrates on discriminative learning to distinguish between classes. The resulting learning algorithm integrates the advantages of both generative learning and discriminative learning. Because it uses CL dependence-tree estimation, we call our algorithm CL-Net. Empirical tests indicate that the proposed learning algorithm makes significant improvements when compared with the related classifiers that are constructed by either generative learning or discriminative learning.
758563
Cost-sensitive boosting for classification of imbalanced data
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a successful meta-technique for improving classification accuracy. The insight gained from a comprehensive analysis of the AdaBoost algorithm in terms of its advantages and shortcomings in tacking the class imbalance problem leads to the exploration of three cost-sensitive boosting algorithms, which are developed by introducing cost items into the learning framework of AdaBoost. Further analysis shows that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss. These boosting algorithms are also studied with respect to their weighting strategies towards different types of samples, and their effectiveness in identifying rare cases through experiments on several real world medical data sets, where the class imbalance problem prevails.
39329211
Correction to "Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data"
2358468
Human Action Recognition by Semilatent Topic Models
We propose two new models for human action recognition from video sequences using topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". Our models differ from previous latent topic models for visual recognition in two major aspects: first of all, the latent topics in our models directly correspond to class labels; second, some of the latent variables in previous topic models become observed in our case. Our models have several advantages over other latent topic models used in visual recognition. First of all, the training is much easier due to the decoupling of the model parameters. Second, it alleviates the issue of how to choose the appropriate number of latent topics. Third, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification results on five different data sets. Our results are either comparable to, or significantly better than previously published results on these data sets.
117805231
Hierarchical methods for three-dimensional surface reconstruction
Surface reconstruction from raw data such as volume data, implicit functions, and scattered data in three-dimensional space is very useful in medical imaging, scientific visualization, and geometric modeling. In this study, several surface reconstruction techniques are investigated. A new surface reconstruction technique and several interpolation schemes are introduced for processing the volume data. A new hierarchical scheme for extracting three-dimensional surfaces from volume data is presented. The hierarchical scheme is used to visualize different levels of surface detail allowing a user to explore different features of the surface at different scales. Estimated surface curvature is used as a smoothness condition, and the distance measurement between different levels of surfaces constructed is used as a stopping criterion. The volume pyramid used by the hierarchical scheme was built and tested by two different data structures: an octree and wavelet transforms. The hierarchical method using three-dimensional wavelet transforms is also useful in multiresolution surface analysis. An adaptive hierarchical method using an octree is implemented to increase the accuracy of the surface by adaptively subdividing regions of high curvature using an appropriate interpolation scheme without substantially increasing the number of polygons.
1200220
Boosting over Groups and Its Application to Acronym-Expansion Extraction
In many real-world classification applications, instances are generated from different `groups'. Take webpage classification as an example, the webpages for training and testing can be naturally grouped by network domains, which often vary a lot from one to another in domain size or webpage template. The differences between `groups' would result that the distribution of instances from different `groups' also vary. Thus, it is not so reasonable to equally treat the instances as the independent elements during training and testing as in conventional classification algorithms. This paper addresses the classification problem where all the instances can be naturally grouped. Specifically, we give a formulation to this kind of problem and propose a simple but effective boosting approach, which is called AdaBoost.Group. The problem is demonstrated by the task of recognizing acronyms and their expansions from text, where all the instances are grouped by sentences. The experimental results show that our approach is more appropriate to this kind of problems than conventional classification approaches.
13987121
A Unified Framework for Segmentation-Assisted Image Registration
This paper presents a unified variational framework for seamlessly integrating prior segmentation information into non-rigid registration procedures. Under this framework, in addition to the forces arise from the similarity measure in seeking for detailed correspondence, another set of forces generated by the prior segmentation contours can provide an extra guidance in assisting the alignment process towards a more meaningful, stable and noise-tolerant procedure. Local correlation (LC) is being used as the underlying similarity measures to handle intensity variations. We present several 2D/3D examples on synthetic and real data.
10165712
A Novel Secure Routing System in Overlay Environment
Routers are effective building blocks for Internet. It is almost impossible to modify router software only for applications. How to satisfy applications’ specific requirements of communication is a big problem. Now more and more systems begin to place application oriented routing service on overlay networks. This paper compares several overlay routing systems based on route number and brings forward a novel routing system based on trust model. System design principles are discussed and system simulation performance is analyzed. The results show that the system is able to provide secure routing service to applications in the case of vicious nodes.