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IJCNN 2012 Competition: Classification of Psychiatric Problems Based on Saccades Włodzisław Duch 1,2, Tomasz Piotrowski 1 and Edward Gorzelańczyk 3 1 Department of Informatics, Nicolaus Copernicus University, Toruń, Poland 2 School of Computer Engineering, Nanyang Technological University, Singapore 3 Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
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Are we doing the right thing? Perhaps simplest approach is sufficient?
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Perhaps the data is trivial?
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Data mining packages No free lunch => provide different type of tools for knowledge discovery: decision tree, neural, neurofuzzy, similarity-based, SVM, committees, tools for visualization of data. Support the process of knowledge discovery/model building and evaluating, organizing it into projects. Many other interesting DM packages of this sort exists: Weka, Yale, Orange, Knime... >170 packages on the-data-mine.com list! We are building Intemi, radically new DM tools. GhostMiner, data mining tools from our lab + Fujitsu: http://www.fqspl.com.pl/ghostminer/ http://www.fqspl.com.pl/ghostminer/ Separate the process of model building (hackers) and knowledge discovery, from model use (lamers) => GM Developer & Analyzer
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What DM packages do? Hundreds of components... transforming, visualizing... Rapid Miner 5.2, type and # components: total 712 (March 2012) Process control 38 Data transformations 114 Data modeling 263 Performance evaluation 31 Other packages 266 Text, series, web... specific transformations, visualization, presentation, plugin extensions... ~ billions of models! Visual “knowledge flow” to link components, or script languages (XML) to define complex experiments.
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Test the simplest methods with O(nd) complexity & use them first in meta-learning.Test the simplest methods with O(nd) complexity & use them first in meta-learning. Provide reference results.Provide reference results. Compare with state-of-the-art results.Compare with state-of-the-art results. Identify trivial cases.Identify trivial cases. Provide reference results.Provide reference results. Compare with state-of-the-art results.Compare with state-of-the-art results. Identify trivial cases.Identify trivial cases.
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Six O(nd) classifiers (n = # samples, d= dimensionality). 1.MC, Majority Classifier.
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2. 1NP, Nearest Prototype classifier Prototypes taken from class means, Euclidean distance only.
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Thank you for lending your ears! Thank you for lending your ears! Google: W. Duch => Papers & presentations & more
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