7205040
Effective Data Mining Using Neural Networks
Classification is one of the data mining problems receiving great attention recently in the database community. The paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classification rules are generated using the result of this analysis. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems.
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.
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.
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.
6969845
Real-time Application Monitoring and Diagnosis for Service Hosting Platforms of Black Boxes
Service hosting platforms typically run a large number of third-party applications that are composed of multiple communicating components distributed on a dynamic set of hosting servers. Understanding the real-time behaviors of these applications and the intricate interactions/dependency relationships among these application components is very important to service management tasks such as load balancing, capacity planning, performance debugging and fault diagnosis. In this paper, we present the scalable real-time application monitoring and diagnosis (SRAMD) tool, for applications consisting of "black box" components: software without source code available, and usually without desired logging instrumentation. SRAMD runs at application layer and requires no modification to existing applications, middleware, or messages. For each application component collocated at its hosting server, a SRAMD monitor traces the component's packet-level traffic unobtrusively, summarizes its local resource utilization and performance (e.g. response time) online, performs interactive queries (e.g. per- request resource utilization) to locate possible bottlenecks on- demand, and discovers inter-component dependence relationships statistically. The SRAMD controller simply aggregates reports from distributed monitors to construct real-time application topologies with rich runtime information. We have developed mechanisms to decentralize the computation overhead and minimize the communication cost in the monitoring and diagnosis process, and two schemes to discover application component dependency relationships in different scenarios. The SRAMD tool offers an alternative to server logs and message-level traces for service monitoring and performance diagnosis.
15137899
Ensembles of cascading trees
We introduce a new method, called CS4, to construct committees of decision trees for classification. The method considers different top-ranked features as the root nodes of member trees. This idea is particularly suitable for dealing with high-dimensional bio-medical data as top-ranked features in this type of data usually possess similar merits for classification. To make a decision, the committee combines the power of individual trees in a weighted manner. Unlike Bagging or Boosting which uses bootstrapped training data, our method builds all the member trees of a committee using exactly the same set of training data. We have tested these ideas on UCI data sets as well as recent bio-medical data sets of gene expression or proteomic profiles that are usually described by more than 10,000 features. All the experimental results show that our method is efficient and that the classification performance are superior to C4.5 family algorithms.
6076549
Executable JVM model for analytical reasoning: a study
To study the properties of the Java Virtual Machine(JVM) and Java programs, our research group has produced a series of JVM models written in a functional subset of Common Lisp. In this paper, we present our most complete JVM model from this series, namely, M6, which is derived from a careful study of the J2ME KVM [16] implementation.On the one hand, our JVM model is a conventional machine emulator. M6 models accurately almost all aspects of the KVM implementation, including the dynamic class loading, class initialization and synchronization via monitors. It executes most J2ME Java programs that do not use any I/O or floating point operations. Engineers may consider M6 an implementation of the JVM. It is implemented with around 10K lines in 20+ modules.On the other hand, M6 is a novel model that allows for analytical reasoning besides conventional testing. M6 is written in an applicative (side-effect free) subset of Common Lisp, for which we have given precise meaning in terms of axioms and inference rules. A property of M6 can be expressed as a formula. Rules of interference can be used analytically to derive properties of M6 and the Java programs that run on the model, using a mechanical theorem prover.We argue that our approach of building an executable model of the system with an axiomatically described functional language can bring benefits from both the testing and the formal reasoning worlds.
17120587
Combination methods in microarray analysis
Microarray technology and experiment can produce thousands or tens of thousands of gene expression measurements in a single cellular mRNA sample. Selecting a list of informative differential genes from these measurement data has been the central problem for microarray analysis. Many methods to identify informative genes have been proposed in the past. However, due to the complexity of biological systems, each proposed method seems to perform nicely in a particular data set or specific experiment. It remains a great challenge to come up with a selection method for a wider spectrum of experiments and a broader variety of data sets. In this paper, we take the approach of method combination using data fusion and rank-score graph which have been used successfully in other application domains such as information retrieval, pattern recognition and tracking, and molecular similarity search. Our method combination is efficient and flexible and can be extended to become a general learning system for microarray gene expression analysis.
6190656
Toward a Programmatic Semantics of Natural Language
Natural language is imbued with a rich semantics but unfortunately its complex elegance is often mistaken for mere imprecision. Because complete parsers of English are not yet achievable, people assume that it is not feasible to use English directly as a means of instructing computers. However, in this paper, we show that English descriptions of procedures often contain programmatic semantics-linguistic features that can be easily mapped into programming language constructs. Some linguistic features can even inspire new ways of thinking about specifying programs. Far from being hopelessly ambiguous, natural languages exhibit important principles of communication that could be used to make human-computer communication more natural