39329211
Correction to "Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data"
344886
From Association to Classification: Inference Using Weight of Evidence
Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is still seldom found in current publications. Trying to bridge this gap, this paper extends our previous paper on significant event association discovery to classification. We propose to use weight of evidence to evaluate the evidence of a significant event association in support of, or against, a certain class membership. Traditional weight of evidence in information theory is extended here to measure the event associations of different orders with respect to a certain class. After the discovery of significant event associations inherent in a data set, it is easy and efficient to apply the weight of evidence measure for classifying an observation according to any attribute. With this approach, we achieve flexible prediction.
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.
10246554
An Overview of Associative Classifiers
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. This paper presents a critical overview of certain aspects on modelling associative classifiers including event association mining, computational complexity in mining associations, methods of constructing associative classifiers based on the association patterns discovered, some similarities and differences among the reported associative classification systems, and experimental comparisons of these associative classifiers with the conventional classification system such as C4.5.
22289409
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
12250963
Boosting for Learning Multiple Classes with Imbalanced Class Distribution
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. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only scenario where the class imbalance problem prevails. Reported solutions for bi-class applications are not applicable to multi-class problems. In this paper, we develop a cost-sensitive boosting algorithm to improve the classification performance of imbalanced data involving multiple classes. One barrier of applying the cost-sensitive boosting algorithm to the imbalanced data is that the cost matrix is often unavailable for a problem domain. To solve this problem, we apply Genetic Algorithm to search the optimum cost setup of each class. Empirical tests show that the proposed cost-sensitive boosting algorithm improves the classification performances of imbalanced data sets significantly.
206733202
Foreground and Shadow Detection Based on Conditional Random Field
This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.
17238158
A Novel Cookie-Based DDoS Protection Scheme and its Performance Analysis
Seamless handover is one of the most attractive research fields in B3G systems. Many mechanisms are proposed to provide certain QoS guarantees in handovers of mobile systems, which would also introduce new threats, such as DoS and DDOS attacks. In this paper, we extend a cookie-based scheme to protect systems from DoS and DDoS attacks. We also adopt a novel queuing network model to analyze our scheme by estimating two essential metrics, i.e. the mean total response time and the mean queue length of MAP. Numerical results indicate that our mechanism works better than the traditional cookie-based scheme and it could effectively help networks defend against abnormal attacks in the handover process.
7846558
T1DBase, a community web-based resource for type 1 diabetes research
T1DBase (http://T1DBase.org) is a public website and database that supports the type 1 diabetes (T1D) research community. The site is currently focused on the molecular genetics and biology of T1D susceptibility and pathogenesis. It includes the following datasets: annotated genome sequence for human, rat and mouse; information on genetically identified T1D susceptibility regions in human, rat and mouse, and genetic linkage and association studies pertaining to T1D; descriptions of NOD mouse congenic strains; the Beta Cell Gene Expression Bank, which reports expression levels of genes in beta cells under various conditions, and annotations of gene function in beta cells; data on gene expression in a variety of tissues and organs; and biological pathways from KEGG and BioCarta. Tools on the site include the GBrowse genome browser, site-wide context dependent search, Connect-the-Dots for connecting gene and other identifiers from multiple data sources, Cytoscape for visualizing and analyzing biological networks, and the GESTALT workbench for genome annotation. All data are open access and all software is open source.
31735857
Research on Vehicle Image Classifier Based on Concentration Regulating of Immune Clonal Selection
For time-variation and nonlinearity of vehicle image classifier design in complicated environment, a novel classifier based on Immune Clone Concentration Clustering Algorithm (ICCCA) was proposed, inspired by the clone selection theory in the natural immune system. It overcame the defects of traditional classification algorithms as multi-restraint and trap of local optimum. It also avoided the high complexity and regulating dissimilation caused by the cross and variation rules of exiting immune clone clustering algorithms. Directing towards two-class image pattern recognition problem, we defined the antibody and antigen of vehicle image, established class divisibility standard, and designed the affine mathematical model based on the antibody and antigen's Euclidean distance and their concentration. We proposed a method to create immune clone memory cell by the antibodies concentration as long as concentration regulating. The algorithm can guarantee the antibody diversity, and obtain the global optimal solution quickly and precisely. The experimental results verified that the classification accuracy of the algorithm is superior to other classification algorithm.
19740000
Color Image Segmentation Based on Parameter-Dependent Connected Components
A theory of a parameter-dependent connected components of gray images that takes into account both the gray values of the pixels and the differences of the gray values of the neighboring pixels has been introduced in a previous work. The concept of the parameter-dependent connected components has been applied to gray-scaled images for segmentation successfully. In this paper, the author investigates a method to apply the concept of the parameterdependent connected components to color images for segmentation.