12824352
On Issues of Instance Selection
The digital technologies and computer advances with the booming internet uses have ledto massive data collection (corporate data, data warehouses, webs, just to name a few) andinformation (or misinformation) explosion. Szalay and Gray described this phenomenon as“drowning in data” (Szalay and Gray, 1999). They reported that each year the detectors attheCERNparticlecolliderinSwitzerlandrecord1petabyteofdata;andresearchersinareasof science from astronomy to the human genome are facing the same problems and chokingon information. A very natural question is “now that we have gathered so much data, whatdo we do with it?” Raw data is rarely of direct use and manual analysis simply cannotkeep pace with the fast growth of data. Data mining and knowledge discovery (KDD), as anew emerging ﬁeld comprising disciplines such as databases, statistics, machine learning,comes to the rescue. KDD attempts to turn raw data into nuggets and create special edgesin this ever competitive world for science discovery and business intelligence.TheKDDprocessisdeﬁnedinFayyadetal.(1996)as
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.
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.
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.
15034094
Rendering Aesthetic Impressions of Text in Color Space
What is an artwork and how could a machine become artist? This paper addresses the provocative question by theorizing a computational model of aesthetics and implementing the Aesthetiscope|a computer program that portrays aesthetic impressions of text and renders an abstract color grid artwork reminiscent of early twentieth century abstract expressionism. Following Dewey’s psychological interpretation of \aesthetic" and Jung’s ontology of fundamental psychological functions, we theorize that a viewer flnds an artwork moving and satisfying because it seduces her into rich evocations of thoughts, sensations, intuitions, and feelings. The Aesthetiscope embodies this theory and aims to generate color grids paired with inspiration texts (a word, a poem, or song lyrics), which can be received as aesthetic and artistic by a viewer. The paper describes flve Jungian aesthetic readers which are together capable of creative narrative understanding, and three color-logics that employ psycho-semantic principles to render the aesthetic readings in color space. Evaluations of the Aesthetiscope revealed that the program is best at portraying intuition and feeling, and that overall, the Aesthetiscope is capable of creating the aesthetic of art based on an inspiration text in a non-arbitrary way.
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.
7448673
Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients
MOTIVATIONS AND RESULTS
For classifying gene expression profiles or other types of medical data, simple rules are preferable to non-linear distance or kernel functions. This is because rules may help us understand more about the application in addition to performing an accurate classification. In this paper, we discover novel rules that describe the gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. We also introduce a new classifier, named PCL, to make effective use of the rules. PCL is accurate and can handle multiple parallel classifications. We evaluate this method by classifying 327 heterogeneous ALL samples. Our test error rate is competitive to that of support vector machines, and it is 71% better than C4.5, 50% better than Naive Bayes, and 43% better than k-nearest neighbour. Experimental results on another independent data sets are also presented to show the strength of our method.


AVAILABILITY
Under http://sdmc.lit.org.sg/GEDatasets/, click on Supplementary Information.
23957657
Terminology model discovery using natural language processing and visualization techniques
Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.
29257622
Use of Built-in Features in the Interpretation of High-dimensional Cancer Diagnosis Data
Cancer diagnosis data, for example microarray gene expression profiling data and proteomic profiling data, are often described by thousands of features. To computationally make a diagnosis for new samples, these data are usually input to a learning algorithm, the algorithm then induces a classifier, the classifier then predicts a class label for any test sample. As the data is so high-dimensional, most of the resulting classifiers are very complicated particularly those based on kernel-functions such as support vector machines---the interpretation of the decision results must need all the features to be involved. In this paper, we discuss built-in features and use them to concisely characterize the data and to easily interpret the decisions. Built-in features are features that are used only in the classifiers, and that are only a small subset of the original features, e.g., the features in a decision tree. So, the notion of built-in features is different from input features and also from original features. As there is a significant reduction from the huge size of original features to a small number of relevant features, the complexity of the interpretation can be much eased. The use of built-in features also provides much potential for elucidating the translation between raw data and clinically useful knowledge. In this paper, we also report that the performance of classifiers using built-in features tends to remain stable even input feature space changes, but other types of classifiers fluctuate their performance. So, once again, we promote the use of classifiers that use built-in features since the algorithms can avoid the existing hard problem of selecting best number of features for learning.