9357291
A Balanced Ensemble Approach to Weighting Classifiers for Text Classification
This paper studies the problem of constructing an effective heterogeneous ensemble classifier for text classification. One major challenge of this problem is to formulate a good combination function, which combines the decisions of the individual classifiers in the ensemble. We show that the classification performance is affected by three weight components and they should be included in deriving an effective combination function. They are: (1) Global effectiveness, which measures the effectiveness of a member classifier in classifying a set of unseen documents; (2) Local effectiveness, which measures the effectiveness of a member classifier in classifying the particular domain of an unseen document; and (3) Decision confidence, which describes how confident a classifier is when making a decision when classifying a specific unseen document. We propose a new balanced combination function, called dynamic classifier weighting (DCW), that incorporates the aforementioned three components. The empirical study demonstrates that the new combination function is highly effective for text classification.
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...
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.
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.
13024009
Building effective defect-prediction models in practice
Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. Effective defect prediction models can help developers focus quality assurance activities on defect-prone modules and thus improve software quality by using resources more efficiently. These models often use static measures obtained from source code, mainly size, coupling, cohesion, inheritance, and complexity measures, which have been associated with risk factors, such as defects and changes.
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.
6157335
Beating Common Sense into Interactive Applications
A long-standing dream of artificial intelligence has been to put commonsense knowledge into computers -- enabling machines to reason about everyday life. Some projects, such as Cyc, have begun to amass large collections of such knowledge. However, it is widely assumed that the use of common sense in interactive applications will remain impractical for years, until these collections can be considered sufficiently complete and commonsense reasoning sufficiently robust. Recently, at the Massachusetts Institute of Technology's Media Laboratory, we have had some success in applying commonsense knowledge in a number of intelligent interface agents, despite the admittedly spotty coverage and unreliable inference of today's commonsense knowledge systems. This article surveys several of these applications and reflects on interface design principles that enable successful use of commonsense knowledge.
11306780
Mean-entropy discretized features are effective for classifying high-dimensional biomedical data
This paper studies an empirical feature selection heuristics for classifying high-dimensional bio-medical data. A feature's discriminating power can be measured by its entropy value. Based on this idea, we do not consider those features that are ignored by the entropy idea. Such a selection can usually reduce the dimensionality of the data by 90-95%. Then we rank the remaining features, and select features whose entropy is smaller than the average of all the remaining features' entropies. This round of selection can usually further reduce two thirds of the features. So, we can achieve a reduction from tens of thousands of features to only hundreds of important features. Furthermore, we also observe that learning algorithms, including our new tree-committee classifier, generally improve their accuracy after the feature selection. This heuristics appears to be more systematic than the prevailing use of specific numbers of top-ranked features for classification.
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.