5517595
A connectionist approach to generating oblique decision trees
Neural networks and decision tree methods are two common approaches to pattern classification. While neural networks can achieve high predictive accuracy rates, the decision boundaries they form are highly nonlinear and generally difficult to comprehend. Decision trees, on the other hand, can be readily translated into a set of rules. In this paper, we present a novel algorithm for generating oblique decision trees that capitalizes on the strength of both approaches. Oblique decision trees classify the patterns by testing on linear combinations of the input attributes. As a result, an oblique decision tree is usually much smaller than the univariate tree generated for the same domain. Our algorithm consists of two components: connectionist and symbolic. A three-layer feedforward neural network is constructed and pruned, a decision tree is then built from the hidden unit activation values of the pruned network. An oblique decision tree is obtained by expressing the activation values using the original input attributes. We test our algorithm on a wide range of problems. The oblique decision trees generated by the algorithm preserve the high accuracy of the neural networks, while keeping the explicitness of decision trees. Moreover, they outperform univariate decision trees generated by the symbolic approach and oblique decision trees built by other approaches in accuracy and tree size.
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.
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.
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.
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.
267445
Biological Nomenclatures: A Source of Lexical Knowledge and Ambiguity
There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.
45781537
Lower bounds on the signed k-subdomination number of graphs
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.
14433108
Using amino acid patterns to accurately predict translation initiation sites
The translation initiation site (TIS) prediction problem is about how to correctly identify TIS in mRNA, cDNA, or other types of genomic sequences. High prediction accuracy can be helpful in a better understanding of protein coding from nucleotide sequences. This is an important step in genomic analysis to determine protein coding from nucleotide sequences. In this paper, we present an in silico method to predict translation initiation sites in vertebrate cDNA or mRNA sequences. This method consists of three sequential steps as follows. In the first step, candidate features are generated using k-gram amino acid patterns. In the second step, a small number of top-ranked features are selected by an entropy-based algorithm. In the third step, a classification model is built to recognize true TISs by applying support vector machines or ensembles of decision trees to the selected features. We have tested our method on several independent data sets, including two public ones and our own extracted sequences. The experimental results achieved are better than those reported previously using the same data sets. Our high accuracy not only demonstrates the feasibility of our method, but also indicates that there might be "amino acid" patterns around TIS in cDNA and mRNA sequences.