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.
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.
16781216
Fast hierarchical clustering and its validation
Clustering is the task of grouping similar objects into clusters. A prominent and useful class of algorithm is hierarchical agglomerative clustering (HAC) which iteratively agglomerates the closest pair until all data points belong to one cluster. It outputs a dendrogram showing all N levels of agglomerations where N is the number of objects in the dataset. However, HAC methods have several drawbacks: (1) high time and memory complexities for clustering, and (2) inefficient and inaccurate cluster validation. In this paper we show that these drawbacks can be alleviated by closely studying the dendrogram. Empirical study shows that most HAC algorithms follow a trend where, except for a number of top levels of the dendrogram, all lower levels agglomerate clusters which are very small in size and close in proximity to other clusters. Methods are proposed that exploit this characteristic to reduce the time and memory complexities significantly and to make validation very efficient and accurate. Analyses and experiments show the effectiveness of the proposed method.
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...
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.
2204750
A model of textual affect sensing using real-world knowledge
This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.
8038499
Workshop Report
This is a report on a workshop held at Cornell University on Ma rch 30 – April 1, 2015. The workshop was held to honor Prof. Donald Farley on the occasio n of his 80th birthday (belatedly) and also to consider the future of radio and space physics. Ab out 60 scientists representing institutions in North America, South America, and Europe attende d. Participants included some of the most senior scientists in the field, mid-career scientists, early-career scientists, and students. Along with radio scientists, the workshop was attended by NASA inv estigators, theorists, modelers, and a representative from NSF. The focus of the workshop was the state of the AGS facilities. The first day of the workshop was retrospective in nature, and the second day considered cont emporary research being performed with the facilities. The third day focused on discussions about t he future. The discussions were vigorous and broad and extended to the vitality of not just the facilit ies but to geospace and heliospheric science generally. A recurring theme of the workshop was the fact that incoheren t scatter remains, by far, the most accurate and precise technique for studying the upper atmos phere, the standard by which other experimental techniques, ground-based and in situ, are valid ated and calibrated. Incoherent scatter experiments yield unambiguous estimates of the most import ant state parameters in the upper atmosphere with fine spatio-temporal resolution and offer the possibility of measurements with longterm continuity. Only sounding rockets make measurements w ith superior resolution, but sounding rockets are most often launched nearby incoherent scatter r dars since the latter provide critical experimental context. The incisive nature of incoherent scatter radar measuremen ts will be crucial for resolving fundamental problems in geospace physics. For example, the E region/ lower thermosphere is a complicated region of geospace that remains poorly understood . This is the region where turbulence transitions to laminar flow, where molecular oxygen is disso ciated, where ionization becomes abundant, where ions are unmagnetized whereas electrons are mag netized, and where strong neutral shears exist and electric currents flow. The dynamo fields whi ch drive the ionosphere are generated here and modulated by neutral waves in a complicated way whic h couples the upper atmosphere to the lower atmosphere. Neutral atmospheric waves break he re, but which waves penetrate and where the energy goes is unclear. Sporadic ionization layer s which have grave influence on radio propagation exist here, but their generation mechanism and the influence of the underlying neutral dynamics is not understood. Ground-based remote sensing us i our best experimental tool will be vital in elucidating the processes at work here. Much of the discussion concerned the dichotomy between basi c and applied research at the facilities, the two axes that define Pasteur’s quadrant. Examp les of basic research presented at the workshop included observations of heretofore unseen waves nd turbulence in the lower thermosphere made using broadband measurements of the full incohe rent scatter spectrum at Arecibo and HF interferometry at Jicamarca, observations of extraordi narily high-altitude irregularities over Jicamarca, and methods for inferring high-definition convect ion and current patterns with the PFISR. Applied research with the facilities includes long-durati on, 24-7 observations of basic state parameters used to drive global space-weather models.
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.
15390292
Selection of patient samples and genes for outcome prediction
Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computational method for outcome prediction. Our idea is to use an informative subset of original training samples. This subset consists of only short-term survivors who died within a short period and long-term survivors who were still alive after a long follow-up time. These extreme training samples yield a clear platform to identify genes whose expression is related to survival. To find relevant genes, we combine two feature selection methods - entropy measure and Wilcoxon rank sum test - so that a set of sharp discriminating features are identified. The selected training samples and genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a survival/relapse risk score for drawing Kaplan-Meier survival curves. We apply this method to two data sets: diffuse large-B-cell lymphoma (DLBCL) and primary lung adenocarcinoma. In both cases, patients in high and low risk groups stratified by our risk scores are clearly distinguishable. We also compare our risk scores to some clinical factors, such as International Prognostic Index score for DLBCL analysis and tumor stage information for lung adenocarcinoma. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.
9642703
Discovering typical structures of documents: a road map approach
The structure of a document refers to the role and hierarchy of subdocument references. Many online documents are similarly structured, though not identically structured. We study the problem of discovering “typical” structures of a collection of such documents, where the user specifies the minimum frequency of a typical structure. We will consider structural features of subdocument references such as labeling, nesting, ordering, cyclicity, and wild-card references, like those found on the Web and digital libraries. Typical structures can be used to serve the following purposes. (a) The “table-ofcontent” for gaining the general information of a source. (b) A road map for browsing and querying a source. (c) A basis for clustering documents. (d) Partial schemas for building structured layers to provide standard database access methods. (e) User/customer’s interests and browsing patterns. We present a solution to the discovery problem.