Jornada de Seguimiento de Proyectos en Tecnologías del Software

Jornadas de Seguimiento de Proyectos, 2007
Programa Nacional de Tecnologías Informáticas
jspTIN@CEDI2007: Jornada de
Seguimiento de Proyectos de Tecnologías
Informáticas
TIN2005-08832-C03
Rafael Morales-Bueno *
Coordinador e IP C03-01
Ricard Gavaldá ***
IP C03-03
Elvira Mayordomo **
IP C03-02
Abstract
This project will develop, implement, and experimentally validate formal concepts
and algorithms that can be applied to the analysis, modelling, and prediction or learning from
very large data sets represented as sequences.
Some of the models that will be considered are:
1) The Data Stream model, as far as it can be applied to Data Mining and Business Intelligence
tasks,
2) Models for lossless data compression and on-line prediction,
3) Event sequences and Temporal Series,
4) Proximity-based models (e.g., Nearest-Neighbours-like),
5) Models based on decision and regression trees, and
6) Grammatical or generative models.
In particular, we are interested in models that can learn drifting concepts and in the
presence of noise.
For most of these models there will be a first phase for theoretical development, a
second phase for implementation and experimental testing of the algorithms, and a third phase
of application to real data.
Keywords:
Data mining, knowledge discovery, computational learning, symbolic sequences,
algorithms, complexity, Hausdorff dimension.
* Email: rmorales@uma.es
** Email: elvira@unizar.es
***
Email: gavalda@lsi.upc.edu

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1 Objetivos del proyecto
The objectives of this Project is a continuation of the previous and linked Projects: FRESCO
(2000-2002) and MOISES (2003-2005):
1. Design of algorithms to handle Data Streams that vary over time (Data Mining on Time
Varying Data Streams), using extensions of sequential sampling methods. Theoretical analysis
of the efficiency and accuracy of these algorithms, and evaluation over empirical and real data.
We will give higher priority to learning techniques in objectives 5 and 8.
2. Continuation of work on complex pattern handling in event sequences and relational
dependencies. Development of new theoretical characterizations that help in the design of
algorithms that work for patterns that are more complex than those that are feasible today
and/or that have better computational efficiency.
3. Proposal, implementation and validation of new lossless compression methods that are less
sensitive to small input variations than known algorithms (Lempel-Ziv and other).
4. Validation of efficiency and performance of the four existent compression algorithms for
biological sequences. Application to DNA sequence classification.
5. Design of new decision tree and regression learning algorithms, with the possibility of including
evolutionary computation and the following characteristics, adaptability to concept change,
incrementality and noise robustness.
6. Improvement of event prediction algorithms with generation of attributes computed from the
set of differential equations that describe parts of the phenomenon, incrementality, adaptability
to time pattern change, and noise robustness.
7. Definition, analysis and inclusion of techniques of significant variable selection in time series
using Fourier models, sliding window models, and spline adjustment.
8. Development of learning models based on sample grouping and classification by the closest
neighbour method, study of methods and group representation, and distance functions.
9. Continuation of work on identification of dimension, prediction, and compression.
Improvement of dimension computing techniques.
10. Study of different performance evaluation measures of sequence compression and prediction
algorithms.
11. Study of grammatical models. Extension of present models based on proof nets (and in
particular non commutative logic nets) to the study of language syntax. Extension of the
present theories on parsing of mildly context sensitive grammars to more general cases.
12. Definition and study of Data Streams as a computation model. Investigation of the complexity
of computing functions in this model, using methods from Complexity Theory and
connections with compression data models and succinct structure representation.
13. Study of models for the construction of time and space reasoning of common sense based on
sensory and motor experience of intelligent agents, for instance even sequence collection.
Connected with objective 5.
14. Application of the developed algorithms to real data on labor disability, gender violence,
students, DNA, and data from private enterprises that are “EPO” (promoting and observing
organization).
The resources to achieve these goals are people, computers and data sets.
Initially there was 23 people participating in this project (6 from Zaragoza and Valladolid, 7 from
Barcelona and 10 from Málaga). A participant from Málaga (Manuel Baena) obtained a FPI grant

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from MEC by starting on May 2007. Recently (June-2007) another participant from Malaga (José
del Campo), that has finished his FPI grant from the previous coordinated project 2003-2005, has
obtained his Ph. D. Degree and we think that, during the project, 2 or 3 students will obtain it too.
Two new people are working in the subproject of Málaga since March 2007: Amparo Ruiz and
Francisco Cantalejo.
Since February 2006 Pilar Albert is involved as a new member of the Zaragoza
subproject, by means of a grant from Aragón Government, to work in topics related to the project.
Since January 2007, Philippe Moser is involved as a new member of the Zaragoza subproject by
means of a contract in the “Juan de la Cierva” program.
We emphasize the participation in this project of Professor Jack Lutz, from Iowa State
University, in USA. This researcher is worldwide known for his contributions to Computational
Complexity and Information Theory, especially in the topics related to Effective Hausdorff
Dimension, that he started in 2000. His collaboration is very important for the attainment of the
results of this project. From 1991 we have collaborated with him, funded by different sources,
including National Science Foundation from the USA government and the Spanish Ministry of
Education.
Since January 2007, Albert Bifet is involved as a new member of the Barcelona subproject
by means of a direct contract. In April 2007, Albert Bifet obtained a FPI grant from Generalitat de
Catalunya (regional government) in order to work in topics related to the project.
Related to computers resources, every subproject has sufficient equipment in order to process the
data sets.
We are working with data sets from public repositories, data sets automatically generated and real
data sets, from poll to tourists in Andalusia, and mental health data.
The chronogram proposed in the project was the following:
Activities/Tasks
First year Second year Third year
ACTIVITY 1 X| | | | | |X| | | | X| | | | |X| | | | | X| | | | |X| | | |X|
WEB X|X|X|X|X|X| | | | |X| | | | | | | | | |X| | | | | | | | |
ST-T X|X|X|X|X|X|X|X|X| | | | | | | | | | | | | | | | | | | | |
PR-ST-T | | | | | | | | | | X|X|X|X|X|X|X|X|X| | | | | | | | | | |
AP-ST-T | | | | | | | | | | | | | | | | | | | | X|X|X|X|X|X|X|X|X|
VC-T X|X|X|X|X|X|X|X|X| | | | | | | | | | | | | | | | | | | | |
PR-VC-T | | | | | | | | | | X|X|X|X|X|X|X|X|X| | | | | | | | | | |
AP-VC-T | | | | | | | | | | | | | | | | | | | | X|X|X|X|X|X|X|X|X|
AD-T X|X|X|X|X|X|X|X|X| | | | | | | | | | | | | | | | | | | | |
PR-AD-T | | | | | | | | | | X|X|X|X|X|X|X|X|X| | | | | | | | | | |
AP-AD-T | | | | | | | | | | | | | | | | | | | | X|X|X|X|X|X|X|X|X|
PC-T X|X|X|X|X|X|X|X|X| | | | | | | | | | | | | | | | | | | | |
PR-PC-T | | | | | | | | | | X|X|X|X|X|X|X|X|X| | | | | | | | | | |
AP-PC-T | | | | | | | | | | | | | | | | | | | | X|X|X|X|X|X|X|X|X|
DPC
X|X|X|X|X|X|X|X|X| X|X|X|X|X|X|X|X|X| X|X|X|X|X|X|X|X|X
TCD
X|X|X|X|X|X|X|X|X| X|X|X|X|X|X|X|X|X X|X|X|X|X|X|X|X|X|
RCP
X|X|X|X|X|X|X|X|X| X|X|X|X|X|X|X|X|X| X|X|X|X|X|X|X|X|X|

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ACTIVITY 1: To coordinate and document (participants: Elvira Mayordomo-EM, Ricard
Gavaldà-RG, Rafael Morales-RM). This will be done during the whole project. At the beginning of
every activity block there will be a basic check between the above mentioned co-ordinators in order
to synchronize the different activities and to produce the documentation stating the results
achieved so far. The responsible persons are the IP of each subproject.
ACTIVITY WEB: To develop the web-site to be used by the project members and by the
scientific community (participants: Rafael Morales-RM, Elvira Mayordomo-EM, Ricard Gavaldà-
RG, José del Campo-JC, Raul Fidalgo-RF, Manuel Baena-MB). The technician required for the
subproject MOISES-MA will do this task and will later participate in the activities related to
programming and applications.
- DEVELOPMENT OF FORMALISMS AND ALGORITHMS
ACTIVITY ST-T: To study new models for time series (participants: Llanos Mora-LM,
Inmaculada Fortes-IF, Francisco Triguero-FT, José Luis Triviño-JLT, Marlon Núñez-MN).
Definition, analysis and incorporation of methods for selecting meaningful variables, so that the
model will be able to learn from the meaningful information and omit useless data. We propose to
study Fourier methods, splines adjustments and sliding windows. We will use the developed
models in the analysis of stationary and non stationary time series.
ACTIVITY VC-T: To define new models based on the nearest neighbour classification method
(participants: José Luis Triviño-JLT, Rafael Morales-RM, Gonzalo Ramos-GR, Francisco Triguero-
FT). Definition of a new learning model based on grouping of samples and the nearest neighbour
classification method. To specify the features of the new model. In this activity the grant holder
(FPI) required for subproject MOISES-MA will participate.
ACTIVITY AD-T: To develop new algorithms to obtain decision trees (participants: Gonzalo
Ramos-GR, José del Campo-JC, Manuel Baena-MB, Inmaculada Fortes-IF, Alejandra Cabaña-AC).
We propose to try to include evolutionary computing concepts for voting algorithms; to build sets
of trees and to include the adaptive sample methos in order to discover changes of concepts and
incrementality. The company “AXPE Consulting” has shown its interest in this activity (see letter
adjoined).
ACTIVITY PC-T: To study models of pattern learning that change over time (participants:
Marlon Núñez-MN, Raúl Fidalgo-RF, Llanos Mora-LM, José del Campo-JC, Alejandra Cabaña-
AC, FPI required for MOISES-MA). To define new learning methods to build regression and
decision incremental trees without adjustment of parameters. To amplify the BPL method by
including incementality and adaptability to changing patterns. The company “AXPE Consulting”
has shown its interest in this activity (see letter adjoined).
ACTIVITY DPC: To study the identification between dimension, prediction and compression in
polynomial time (participants: Elvira Mayordomo-EM, María López-ML, Argimiro Arratia-AA).
To define a compressor that “doesn’t work from zero”. To adjust the parameters that guarentee
the equivalence between compressors and predictors “on-line”. To study the case of compressors

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that parse the input. To study other parameters used in computational learning, like the Vapnik-
Chervonenkis (VC) dimension.
ACTIVITY TCD: To improve methods for computing dimension (participants: Jack Lutz-JL,
Elvira Mayordomo-EM, María López-ML, Alejandra Cabaña-AC). To build an effective version of
the Jarník’s theorem related to diofantic approximations and applications. To apply the equivalence
between compression and dimension to compute the dimension of the Takagi’s function. To study
the influence of the changes of the alphabet and the base in the effective dimension and
compression.
ACTIVITY RCP: To study several performance measures in the algorithms of compression and
sequences’ prediction (participants: Jack Lutz-JL, Elvira Mayordomo-EM, María López-ML). To
study non-linear compression measures (that is, for finer distinction in the reachable dimension).
To study loss functions non logarithmic for prediction. To study scaled dimension.
ACTIVITY LZ: To study the difficulties occurring within the Lempel-Ziv’s algorithms, the onebit
catastrophe (participants: Elvira Mayordomo-EM, Jack Lutz-JL, María López-ML, Alejandra
Cabaña-AC). To study the effect of the compression of little variations in the input. To study the
problem statistically. To study the same problem for other variants of the algorithm. To look for a
formal and satisfactory explanation. In this activity the grant holder (FPI) required for the
subproject MOISES-ZVA will participate.
ACTIVITY FD: To design algorithms for mining Data Stream that change over time (participants:
Ricard Gavaldà-RG, Computer Science engineering and FPI required for MOISES-BAR). To
review known techniques. To select concrete algorithms (by including at least, naive Bayes, the
nearest neighbour classification method and decision trees). Theoretical design of the algorithms
and analysis of their performance. In the activity José del Campo-JC will collaborate.
ACTIVITY SE: To design algorithms for mining temporal patterns in sequences of events, and to
study complex dependences in the relational model. (participants: Jaume Baixeries-JG, G. Casas-
GC and Computer Science engineering required for MOISES-BAR). In this activity tools coming
from the Logic, Data Base theory and Formal Concept Analysis will be used.
ACTIVITY MG: To study grammatical models and applications for the syntax of symbolic
sequences (participants: Glyn Morrill-GM, Mario Fadda-MF, María Luisa González-MLG). To
study the applications of proof nets in the generalized non commutative logic . To study mildly
context-sensitive grammars.
ACTIVITY RT: To define methods to build concepts of temporal and spatial reasoning of
common sense based upon sensorial and motion experience of intelligent agents. (participants:
Josefina Sierra-JS). Raúl Fidalgo-RF collaborates in this activity.
ACTIVITY MC-FD: To study Data Stream as a computing model (participants: Ricard Gavaldà-
RG, Elvira Mayordomo-EM, Antoni Lozano-AL, Argimiro Arratia-AA, Mª Luisa González-MLG).
To research the complexity of computing functions in this model, by using tools from the

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Complexity Theory and to study relations with the model of data compression and succinct
representation of structures.
- PROGRAMMING AND EMPIRICAL EVALUATION OF ALGORITHMS
For each of the following activities: ST-T, VC-T, AD-T, PC-T, FD, SE, MG, RT, there will be an
activity with the same name preceded by “PR-“. These new activities are devoted to the
programming of the algorithms and to its evaluation.
ACTIVITY EVAL-DNA: To validate the performance of the four known algorithms for
compression of biological sequences. Application to the algorithms for classifying DNA sequences.
(participants: Elvira Mayordomo-EM, Computer Science engineering required in MOISES-ZVA).
In this activity the grant holder (FPI) required for the subproject MOISES-ZVA will participate,
and Manuel Baena-MB will collaborate also. The company “DECODE genetics” has shown its
interest in this activity (see letter adjoined).
ACTIVITY-MLZ: To define, program and validate new methods based upon the basic Lempel-
Ziv. (participants: Elvira Mayordomo-EM, Science engineering required in MOISES-ZVA) In this
activity the grant holder (FPI) required for the subproject MOISES-ZVA will participate. Two
companies related to “Software development” have shown their interest in this activity.
ACTIVITY REC: To collect data sets (participants: all members of the project). To collect large
real data sets from companies (public and private).
- APPLICATIONS
For each single activity that develops new programs we propose an activity with the same name
preceded by “AP-“, that is devoted to the application of these programs to real data.
- COORDINATION
There will be two meetings every year to coordinate the activities: One will be in person and the
other by videoconference. The objectives are twofold:
1) To exchange results, implementations and data
2) To adjust, synchronize and refine the subsequent tasks.
Before collecting the data sets, we will have a session in order to unify the representation of data
(XML, gzip, etc), allowing to share the data sets between all the members of the project.
2 Nivel de éxito alcanzado en el proyecto
It can be considered that the objectives have been completed around 60%. More precisely, the
objectives 1-7 and 9-12 are in a good level of development (more that 50%) and the objectives 8,
13 y 14 are in a low level (however, there is 50% time left until project completion). Moderate

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difficulties have arisen when the experimental results do not match the theoretical quality of the
methods; in these cases we have reconsidered our estimations and approximations and modified
some aspects of the algorithms. The relevance of the results can be verified by the impact factor of
the journals or by the acceptation rate of the international conferences (for example, last IDA
conference has a rate of 34%).
Related specially to objective 14, we are recollecting data from labor disability, gender violence,
mental health and tourist poll. From September to the project completion we will apply the
algorithms developed to these data sets and we will analyze the results with experts in every
scientific/economic field.
3 Indicadores de resultados
3.1 ZARAGOZA Subproject (with Valladolid and Iowa)
Objective 3:
María López [7] has studied the sensitiveness of known algorithms to finite variations on
input, by pointing out that they can appear only in some cases. In the future we will obtain a family
of gamblers (compression algorithms) without this sensitiveness.
Pilar Albert, Elvira Mayordomo and Philippe Moser [1] have recently studied compression
algorithms with implementation using pushdown automatas, very used to compress xml archives.
A comparison of compression ratio with respect to Lempel-Ziv algorithms family is in progress.
Objective 4.
Pablo Urcola [11] has developed his Computer Engineering PFC, supervised by Elvira
Mayordomo. This work compares, experimentally, existent compression algorithms for biological
sequences and proposes a new one. Moreover, the influence of compression algorithm used to
build filogenetic trees is studied and experiments with mitochondrial DNA are made.
Objective 9.
Jack Lutz and Elvira Mayordomo have started [8] a new research line about computing of
fractal dimension in situations that do not have been previously considered..
Gu, Lutz and Mayordomo [3] have studied points that appear in computable curves with
finite length (that are the points that can be identified by a nanorobot) from dimension and
Computational Geometry techniques. Lutz and Weihrauch [9] have studied connectivity
properties of set of points with a specific dimension.
Lathrop, Lutz and Summers [5] have studied relations between fractal dimension and selfassembling
structures (very used in nanotechnology).
Objective 10.
In contrast to the best case of algorithmic compression rate, the worse case compression
rate is studied in [2], and their relation with prediction on-line algorithms, fractal dimension and
"gambling".
A wide variety of performance measurements in compression and prediction (different to
error log-loss) have been studied in [4] and [6]. The relation between compression and prediction
has been completely characterized for constant-memory algorithms; the case of polynomial time
algorithms is still open.

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The state of art is summarized by Elvira Mayordomo in the chapter of the book [10] by
including a generalisation that allows the study of virtually every performance measure (in
gambling, compression and prediction).
3.2 BARCELONA Subproject
Objective 1.
Albert Bifet and Ricard Gavaldà have developed the algorithm ADWIN (Adaptive
Windowing) that packs the statistics and data structures necessary to discover drifts in the data
stream and to memorize an updated data sample. This method generates variants of known
learning algorithms that work with data streams, like Naïve Bayes and k-means [14,15] and decision
trees [12].
Another algorithm to detect concept drift has been developed as joint work between
Barcelona and Málaga teams [13].
In both cases, the methods have been evaluated from a theoretical point of view and by
using artificial and/or natural data sets.
This work continues in two directions: the first one is the evaluation of them using real
data in order to refine and to obtain a practical perspective; the second one is the extension of their
use to new learning algorithms.
Objective 2.
A deep work about extracting patterns from trees (ordered and not ordered) has been
done by Balcázar, Bifet and Lozano [20,19,18,17,16]. In the past, only very simple cases had been
solved. The theoretical definition of the problem has been completed and the implementation of
the algorithms is being developed.
Objective 11.
The "survey" paper [22] presents a perspective of the state of art and open problems in
this area. [23,21] present other results on this area of the project.
Objective 12.
This work is in an initial phase. Members of Zaragoza-Valladolid and Barcelona are
studying the relevant bibliography in order to select relevant topics. As first step, they are working
to classify the computational complexity of regular languages in the data stream model, and the
first results are currently being verified.
3.3 MÁLAGA Subproject
Objectives 5, 6 and 7.
We are working to improve the CIDIM algorithm by means of ensemble of classifiers [24]
and by using correction filters [29]. On the other hand, the initial version of an algorithm without
examples memory was presented in [25], and it has been improved in [26] and [27], this last
publication is a collaboration with the University of Porto. The method to discover concept drift
[26] arise from a collaboration between Barcelona and Málaga.
Related to mining of data stream, the Online Tree method has been improved to be
incremental, reducing the processing time needed for every example, but obtaining the same
accuracy. All the improvements have generated a new version OnlineTree 2; and we have sent a

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paper to JMLR [31]. A program to predict solar flares has been developed and evaluated with real
data sets from ESA 2002-2005. The results have been published in [32], [33] and [34].
Two PFC has been developed with real data sets: [35] is the project of Miguel Ángel Zafra
Repiso, whose advisors have been Manuel Baena García and Rafael Morales Bueno, and [36] is the
project of Carlos Rafael Morales Becerra, whose advisors have been José del Campo Ávila and
Gonzalo Ramos Jiménez.
Simultaneously to this activity, three doctoral students of Málaga group have done
research visits to external universities:
- University of Porto (Portugal): José del Campo (3 months in 2006)
- University of Aveiro (Portugal): Manuel Baena (3 months in 2007)
- University of Ljubljana (Slovenia): Raúl Fidalgo is now there (3 months in 2007)
José del Campo has obtained his Ph. D. degree with European Mention [29]
Manuel Baena and Raúl Fidalgo are working in the same direction with
objective date in 2008 (before the completion of this project).
We have applied for a complementary international action of MEC to continue
the research with members of the University of Ljubljana.
4 References
[1] P. Albert, E. Mayordomo, and P. Moser, Bounded pushdown dimension vs Lempel Ziv
information density. ECCC: Electronic Coloquium on Computational Complexity ISSN 1433-
8092, TR07-051 (2007)
[2] K. B. Athreya, J. M. Hitchcock, J. H. Lutz, and E. Mayordomo, Effective strong dimension in
algorithmic information and computational complexity, SIAM Journal on Computing, 37, 671-705
(2007)
[3] X. Gu, J. H. Lutz, E. Mayordomo, Points on computable curves. Proceedings of the Forty-
Seventh Annual IEEE Symposium on Foundations of Computer Science (Berkeley, CA, October
22-24, 2006), IEEE Computer Society Press, pp. 469-474 (2006)
[4] J.M. Hitchcock and M. López-Valdés and E. Mayordomo, Scaled Dimension and the
Kolmogorov Complexity of Turing-Hard Sets, Theory of Computing Systems, to appear (2007)
[5] J. I. Lathrop, J. H. Lutz, and S. M. Summers, Strict self-assembly of discrete Sierpinski triangles,
Computation and Logic in the Real World: Proceedings of the Third Conference on Computability
in Europe (Siena, Italy, June 18-23, 2007), Springer-Verlag Lecture Notes in Computer Science
4497, pp. 455-464 (2007)
[6] M. López-Valdés, Scaled dimension of invidual strings. En Logical Approaches to
Computational Barriers, Local Proceedings of the Second Conference on Computability in Europe
(CiE 2006). Report # CSR 7-2006, University of Wales Swansea, pp. 206-215 (2006)

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[7] M. López-Valdés, Lempel-Ziv Dimension for Lempel-Ziv compression. Proceedings of the
Thirtieth International Symposium on Mathematical Foundations of Computer Science (Bratislava,
Slovakia, August 28 - September 1, 2006), Springer-Verlag, pp. 471-479 (2006)
[8] J. H. Lutz and E. Mayordomo, Dimensions of points in self-similar fractals submitted (2007)
[9] J. H. Lutz and K. Weihrauch, Connectivity properties of dimension level sets, Proceedings of
the Fourth International Conference on Computability and Complexity in Analysis (Siena, Italy,
June 16-18, 2007)
[10] E. Mayordomo, Effective fractal dimension in algorithmic information theory. En "New
Computational Paradigms: Changing Conceptions of What is Computable", Springer-Verlag, to
appear (2007)
[11] P. Urcola, Algoritmos de compresión para secuencias biológicas y su aplicación en árboles
filogénicos construidos a partir de ADN mitocondrial, proyecto fin de carrera dirigido por Elvira
Mayordomo, Universidad de Zaragoza, 2006.
[12] A. Bifet and R. Gavalda: Learning decision trees adaptively from data streams with time drift.
Conference submission, june 2007.
[13] Manuel Baena-García, José del Campo-Ávila, Raúl Fidalgo, Albert Bifet, Ricard Gavaldà and
Rafael Morales-Bueno: "Early Drift Detection Method". ECML-PKDD Workshop on Knowledge
Discovery from Data Streams 2006.
[14] Albert Bifet and Ricard Gavaldà: "Kalman Filters and Adaptive Windows for Learning in Data
Streams ". Proc. 9th International Conference on Discovery Sicence (DS 2006). Springer-Verlag
Lecture Notes in Artificial Intelligence 4265, 29-40.
[15] Albert Bifet and Ricard Gavaldà: " Learning from Time-Changing Data with Adaptive
Windowing". In 2007 SIAM International Conference on Data Mining (SDM'07), Minneapolis,
Minnesota.
[16] José Luis Balcázar, Albert Bifet and Antoni Lozano: "Closed and maximal tree mining using
natural representations". Submitted.
[17] José Luis Balcázar, Albert Bifet and Antoni Lozano: "Mining Frequent Closed Rooted Trees".
Submitted.
[18] José Luis Balcázar, Albert Bifet and Antoni Lozano: "Subtree Testing and Closed Tree Mining
Through Natural Representations ". "Advances in Conceptual Knowledge Engineering" 2007,
Regensburg, Germany.
[19] José Luis Balcázar, Albert Bifet and Antoni Lozano: "Mining Frequent Closed Unordered
Trees Through Natural Representations ". In 2007 International Conference on Conceptual
Structures, Sheffield UK.

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[20] José Luis Balcázar, Albert Bifet and Antoni Lozano: "Intersection Algorithms and a Closure
Operator on Unordered Trees ". Workshop Mining and Learning with Graphs MLG 2006.
[21] G. Morrill, M. Fadda and O. Valentín 2007: "Nondeterministic Discontinuous Lambek
Calculus', in Proceedings of the Seventh International Workshop on Computational Semantics,
IWCS7, Tilburg.
[22] G. Morrill: "Categorial Grammars: Deductive Approaches", Keith Brown (ed.) Encyclopedia
of Language and Linguistics, 2nd Edition, Elsevier, Oxford, Volume 2, 242--248.
[23] Glyn Morrill and Mario Fadda: "Proof Nets for Basic Discontinuous Lambek Calculus",
accepted for Logic and Computation, special issue proceedings of Lambda Calculus, Type Theory
and Natural Language, LCTTNL05, King's College, London, September 2005.
[24] FE-CIDIM: Fast Ensemble of CIDIM Classifiers. G. Ramos, J. del Campo-Ávila, R.Morales.
International Journal of Systems Science, 37, 13 (2006) 939-947.
[25] Incremental Algorithm Driven by Error Margins. G. Ramos, J. del Campo-Ávila, R.Morales.
Lecture Notes in Artificial Intelligence, 4265, (2006) 358-362.
[26] Early Drift Detection Method. M. Baena, José del Campo-Ávila, Albert Bifet, R. Fidalgo,
Ricard Gavaldà, R.Morales. Fourth International Workshop on Knowledge Discovery from Data
Streams, Berlín (Alemania) (2006) 77-86.
[27] Improving prediction accuracy of an incremental algorithm driven by error margins
J. del Campo-Ávila, G.Ramos, R.Morales. Fourth Workshop on Knowledge Discovery from Data
Streams, Berlín (Alemania) (2006) 57-66.
[28] Improving the performance of an incremental algorithm driven by error margins. J. del
Campo-Ávila, G. Ramos-Jiménez, J. Gama y R. Morales-Bueno. Intelligent Data Analysis. 2007. To
appear.
[29] Incremental learning with multiple classifier systems using correction filters for classification. J.
del Campo-Ávila, G. Ramos-Jiménez y R. Morales-Bueno. Lecture Notes in Computer Science del
Intelligent Data Analysis Conference 2007, to appear.
[30] Nuevos Enfoques en Aprendizaje Incremental. PhD These by J. del Campo-Ávila and
supervised by R. Morales-Bueno and G. Ramos-Jiménez
[31] Learning in Environments with Unknown Dynamics: Towards more Robust Concept
Learners. Marlon Nuñez, Raul Fidalgo, Rafael Morales. JMLR (sent).
[32] M. Nuñez y R. Morales, On forecasting the onset of SEP events, Proceedings of the
International Astronomical Union - Symposium 233 (CU Press, 2006).
[33] M. Nuñez y R. Morales, Early Warning of Solar Proton Events, in: Proceedings of the Third
European Space Weather Week, European Space Agency Press, Bruselas (2006).

TIN2005-08832-C03
[34] M. Núñez y R. Morales, Extreme Value Dependence in Problems with a Changing Causation
Structure, Lecture Notes in Computer Science (ADMA 2006), 4093 Springer 2006.
[35] Herramienta para clasificación de correo electrónico basada en Redes Bayesianas. PFC by
Miguel Ángel Zafra Repiso and directed by M. Baena-García and R. Morales-Buenos. January 2007
[36] Minería de datos aplicada a las salud mental, PFC by Carlos Rafael Morales Becerra and
directed by J. del Campo Ávila and G. Ramos Jiménez. July 2007.