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Marco Muselli

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Marco Muselli
Knowledge
from
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FareExtracting
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Biomedical
Data
through
Logic
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dello
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schema
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Learning
Machines
and
Rulex
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Marco_______
Muselli
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Institute of Electronics,
Computer _______
andlivello
Telecommunication Engineering
National Research Council of Italy, Genova, Italy
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[email protected]
Extracting knowledge from data
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Type of knowledge:
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• Correlation, statistical measures
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• Feature ranking, analysis
of relevance
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• Prediction, clustering,
risk analysis
• Intelligible model_____
(rules)
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Basic problem: Infer some knowledge about a biological
phenomenon of interest starting from a sample of data.
K
N
O
W
L
E
D
G
E
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NETTAB 2012
Rule generation methods
Extract models described by a set of intelligible rule in if-then form
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If Pressure > 115 and Heart_rate < 100 then Disease = Yes
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Aggregative approach
Divide-and-conquer approach
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Emphasis on similarities!
Emphasis on differences!
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Statistical vs. Machine learning methods
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Statistical methods
Machine learning methods
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Simpler to be used with huge
• Their application is not
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experience ___
straightforward
and experience is
not so big
Plenty of commercial ______
and
free tools _______
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available
• Commercial tools are often
extensions of statistical packages;
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Limited quantity of knowledge
free programs are not so friendly
extracted
• Relevant quantity of knowledge
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A priori hypotheses on probability
extracted
distributions
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• No a priori hypothesis is required
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Machine learning software
Commercial software
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Free Software
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SAS Enterprise Miner
• WEKA
(www.sas.com/technologies/analytics/
(www.cs.waikato.ac.nz/ml/weka)
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dello
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datamining/miner)
• RapidMiner (rapid-i.com)
IBM SPSS Statistics Software
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(www-01.ibm.com/software/analytics/
• Orange (orange.biolab.si)
spss/products/statistics)
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• Machine Learning & Statistical
Salford Systems Data Mining
Learning in R language
Suite (www.salford-systems.com)
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(cran.r-project.org/web/views/
Statistica Data Miner
MachineLearning.html)
(www.statsoft.com/products/data_____
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mining-solutions)
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RULEX® Suite
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The name RULEX is the contraction of RULe Extraction since it is especially
devoted to generate intelligible
rules, although
a wide range of statistical and
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machine learning approaches will be made available.
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An intuitive graphical interface
allows to
easily
apply standard and advanced
algorithms for analyzing any dataset of interest, providing solution to
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classification, regression and
clustering problems.
The software suite is in rapid evolution; therefore, the number and the
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functionalities of available tasks increase every day.
The suite RULEX® (contraction of RULe Extraction) developed by Impara Srl
(www.impara-ai.com), a spin-off of the National Research Council of Italy, offers a
new simple and powerful tools for extracting knowledge from real world data.
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RULEX GUI
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Dataset
panel
Tasks
Stage
Component
panel
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Logic Learning Machine
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Logistic
Decision trees
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K-nearest-neighbor
Neural networks
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Rulex offers the possibility of applying an original proprietary approach, named
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Logic learning machine (LLM)
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which represents an efficient implementation of the switching neural network
model (Muselli, 2006).
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Besides standard techniques, such as:
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Logic Learning Machine
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The approach of LLM is based on monotone Boolean function synthesis
(Shadow Clustering) and______
adopts an aggregative
policy: at any iteration some
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patterns belonging to the same output class are clustered to produce an
intelligible rule.
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Since the training process occurs in a binary projected space, the
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application of LLM must be
preceded by_______
a discretization
proper cutoffs for ordered (continuous and discrete) input variables.
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LLM allows to solve classification problems producing sets of intelligible rules
capable of achieving an accuracy comparable or superior to that of best machine
learning methods.
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An application in biomedical analysis
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Diabetes: it concerns
the
problemdello
of diagnosing
diabetes
starting from 8 input
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variables; all the 768 considered patients are females at least 21 years old of
Pima Indian heritage: 268
of them are cases
and
500 are controls.
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Dna: it has the aim of recognizing acceptors and donors sites in a primate gene
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sequences with length 60 (basis);
the dataset
consists of 3186 sequences,
subdivided into three classes: acceptor, donor, none.
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Heart: it deals with the detection of heart disease from a set of 13 input
variables concerning patient status; the total sample of 250 elements is formed
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by 120 cases and 150 controls.
The functionalities of Rulex have been verified by analyzing three biomedical
datasets included in the Statlog benchmark:
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An application of Rulex (results)
Five classification algorithms have been considered: LLM, DT,
NN, LOGIT, and KNN.
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Results obtained on an independent test set including 30% of
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data has been compared both in terms of accuracy and of
quantity of knowledge extracted (number of rules and average
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number of conditions). ______
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LLM
DT
Accuracy # Rules # Cond. Accuracy # Rules
NN
LOGIT
KNN
# Cond. Accuracy Accuracy Accuracy
Diabetes
77.40%
14
3.00
73.04%
56
4.02
75.22%
77.23%
69.13%
Dna
94.01%
64
10.86
90.04%
67
6.26
88.69%
92.57%
40.68%
Heart
85.19%
19
5
81.48%
18
3.67
80.25%
83.95%
80.25%
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Conclusions
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An intuitive graphical interface allows to construct complex analysis processes
through the composition of elementary tasks. Facilities for displaying and
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managing datasets are also
provided.
Besides standard methods,____
like
logistic,_______
k-nearest-neighbor,
neural networks
Terzo
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and decision trees, Rulex makes available a new approach, logic learning
machines (LLM), whose models
are described
by intelligible rules.
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Results obtained for the analysis of three biomedical datasets belonging to the
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Statlog benchmark point out
the good quality
of LLM, which achieves an excellent
A new suite, called Rulex, for the analysis of biomedical datasets through
conventional and advanced machine learning techniques has been presented. It is
able to solve classification, regression and clustering problems.
accuracy while providing understandable knowledge about the problem at hand.
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Work in progress
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Functionalities are continuously added to Rulex to improve the versatility of the
suite. Suggestions arising from researchers are extremely important, since they
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allow us to offer a product
satisfying the real
needs of users.
To this aim, we are searching
for researchers
interested to try the Rulex suite,
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signaling bugs and providing us advices for improving each part of the product.
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If you are interested to test
Rulex for your
specific
application, please send me an
email ([email protected]) and we will provide you a fully functional copy
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of Rulex.
Version 2.0 of Rulex is currently under beta testing. Several features have been
added with the intent of giving researchers a simple but powerful tool for
analyzing their own datasets.
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Thanks
fordello
your
attention!
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www.impara-ai.com
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