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.
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
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.
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.
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.
15223911
Boosting with incomplete information
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationship and the motivations behind them. We use auxiliary functions to bound the change of the loss functions and derive explicit parameter update rules for the learning algorithms. We demonstrate encouraging results on two real-world problems --- visual object recognition in computer vision and named entity recognition in natural language processing --- to show the effectiveness of the proposed boosting approach.
21082976
Real-time machine learning in embedded software and hardware platforms
This paper describes on-going research work into real-time machine learning using embedded software and reconfigurable hardware. The main focus of the work is to develop real-time incremental learning methods particularly targeted at demonstration in mobile robot environments. Three main areas are described. The first represents reactive robot navigation knowledge using a novel frequency table technique whose memory requirement is known a priori. The second area investigates a Genetic Algorithm (GA) method that combines planning and reactive approaches to allow navigation to proceed even in the face of time constraints. In the third area we are developing novel hardware-based machine learning systems suitable for implementation in reconfigurable platforms.
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.
882072
Scalable, reliable marshalling and organization of distributed large scale data onto enterprise storage environments
Emerging technologies in high speed NAS, hierarchical storage management systems, and networked systems that virtualize interconnected storage over IP and fiber-channel networks, promise to consolidate distributed data stores onto large-scale professionally managed enterprise storage environments. We describe the software architecture of the PAWN (producer-archive workflow network) environment that enables scalable, reliable marshalling and organization of distributed data into such enterprise storage environments. PAWN was initially developed to capture the core elements required for long term preservation of digital objects as identified by researchers in the digital library and archiving communities. In this paper, we show how PAWN can be extended to enable multiple clients at a number of distributed sites to prepare, organize, and bulk transfer large scale data onto clusters of servers that securely verify the integrity of the data, register the metadata, and store the data into an enterprise storage environment. PAWN allows detailed description, auditing, and organization of the data, and hence allows for efficient management, access, and disaster recovery. The basic software components are based on open standards and Web technologies, and hence are platform independent.
16300996
Research on Optimization Algorithm for Attribute Reduction of Decision System
Attribute reduction is one of the central issue in the theoretical research of rough set, aiming at the NP-hard problem of acquiring minimal reduction and all reduction, an optimization algorithm is proposed to construct reduction tree based on discernibility matrix for acquiring attribute reduction. The discernibility set is acquired by improving discernibility matrix first, then the core and reduction candidate information are obtained, finally the minimal reduction and all reduction are sought fast and effectively by considering attribute frequency as heuristic information, and time cost of the algorithm is analyzed. The time complexity of the algorithm is lower than other presented algorithms.