5882174
Active learning for detecting a spectrally variable subject in color infrared imagery
To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing an interactive computer system based on data-mining techniques with active learning capabilities. Key components of the system are: feature extraction, automatic classification, active learning, and experimental evaluation.
16386059
A Monotonic Measure for Optimal Feature Selection
Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distancebased measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification.
14543078
Resource description framework: metadata and its applications
Universality, the property of the Web that makes it the largest data and information source in the world, is also the property behind the lack of a uniform organization scheme that would allow easy access to data and information. A semantic web, wherein different applications and Web sites can exchange information and hence exploit Web data and information to their full potential, requires the information about Web resources to be represented in a detailed and structured manner. Resource Description Framework (RDF), an effort in this direction supported by the World Wide Web Consortium, provides a means for the description of metadata which is a necessity for the next generation of interoperable Web applications. The success of RDF and the semantic web will depend on (1) the development of applications that prove the applicability of the concept, (2) the availability of application interfaces which enable the development of such applications, and (3) databases and inference systems that exploit RDF to identify and locate most relevant Web resources. In addition, many practical issues, such as security, ease of use, and compatibility, will be crucial in the success of RDF. This survey aims at providing a glimpse at the past, present, and future of this upcoming technology and highlights why we believe that the next generation of the Web will be more organized, informative, searchable, accessible, and, most importantly, useful. It is expected that knowledge discovery and data mining can benefit from RDF and the Semantic Web.
46794790
Analysis of Hidden Representations by Greedy Clustering
The hidden layer of backpropagation neural networks (NNs) holds the key to the networks' success in solving pattern classification problems. The units in the hidden layer encapsulate the network's internal representations of the outside world described by the input data. this paper, the hidden representations of trained networks are investigated by means simple greedy clustering algorithm. This clustering algorithm is applied to networks have been trained to solve well-known problems: the monks problems, the 5-bit problem and the contiguity problem. The results from applying the algorithm to problems with known concepts provide us with a better understanding of NN learning. These also explain why NNs achieve higher predictive accuracy than that of decision-tree methods. The results of this study can be readily applied to rule extraction from Production rules are extracted for the parity and the monks problems, as well as benchmark data set: Pima Indian diabetes diagnosis. The extracted rules from the Indi...
5365583
NeuroRule: A Connectionist Approach to Data Mining
Classification, which involves finding rules that partition a given da.ta set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based symbolic learning methods. The connectionist approach based on neura.l networks has been thought not well suited for data mining. One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans. This paper examines this issue. With our newly developed algorithms, rules which are similar to, or more concise than those generated by the symbolic methods can be extracted from the neural networks. The data mining process using neural networks with the emphasis on rule extraction is described. ExperimenM results and comparison with previously published works are presented.
8367210
Sensor Sequence Modeling for Driving
Intelligent systems in automobiles need to be aware of the driving and driver context. Available sensor data stream has to be modeled and monitored in order to do so. Currently there exist no building blocks for hierarchical modeling of driving. By semi-supervised segmentation such building blocks can be discovered. We call them drivemes in analogy to phonemes. More parsimonious modeling of driving becomes now possible.
1363024
Java Program Verification via a JVM Deep Embedding in ACL2
In this paper, we show that one can “deep-embed” the Java bytecode language, a fairly complicated language with a rich semantics, into the first order logic of ACL2 by modeling a realistic JVM. We show that with proper support from a semi-automatic theorem prover in that logic, one can reason about the correctness of Java programs. This reasoning can be done in a direct and intuitive way without incurring the extra burden that has often been associated with hand proofs, or proofs that make use of less automated proof assistance. We present proofs for two simple Java programs as a showcase.
16336933
Adaptive Video Watermarking Scheme
In this paper, a new adaptive watermarking scheme for uncompressed and compressed video is presented. The proposed method embeds the watermark in DC components by quantizing DC coefficients to reference values, instead of embedding the watermark in AC components. To increase robustness and perceptual invisibility, adaptive quantization control strategy (AQCS) and error compensation strategy (ECS) are introduced. Experimental results demonstrate the proposed watermarking scheme is robust and invisible, and is feasible for real time system implementation.
893749
A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks
Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm
11034725
Data Mining Tools for Biological Sequences
We describe a methodology, as well as some related data mining tools, for analyzing sequence data. The methodology comprises three steps: (a) generating candidate features from the sequences, (b) selecting relevant features from the candidates, and (c) integrating the selected features to build a system to recognize specific properties in sequence data. We also give relevant techniques for each of these three steps. For generating candidate features, we present various types of features based on the idea of k-grams. For selecting relevant features, we discuss signal-to-noise, t-statistics, and entropy measures, as well as a correlation-based feature selection method. For integrating selected features, we use machine learning methods, including C4.5, SVM, and Naive Bayes. We illustrate this methodology on the problem of recognizing translation initiation sites. We discuss how to generate and select features that are useful for understanding the distinction between ATG sites that are translation initiation sites and those that are not. We also discuss how to use such features to build reliable systems for recognizing translation initiation sites in DNA sequences.
24079642
Detecting outliers in irregularly distributed spatial data sets by locally adaptive and robust statistical analysis and GIS
In this paper, we propose a new method for detecting outliers in an irregularly distributed spatial data set. Our method has two desirable properties. First, it is functionally effective due to the introduction of sensitive outlier indices and locally adaptive and robust statistical criteria. Second, it is computationally efficient because of the use of super-block based spatial data sorting and searching scheme. Our method has been implemented using the C programming language and integrated with the Arc/Info GIS system. The integration leads to a powerful exploratory data analysis tool for checking and analysing anomalous values in a GIS environment. Local outliers can be automatically labeled with our method, subject to some user-defined parameters. Outliers represent anomalous or suspicious values in a statistical sense, which may not necessarily be erroneous values. Instead of being simply discarded, statistical outliers should be investigated further using prior qualitative knowledge or in association with other GIS data layers.