Annalisa Appice' Home Page


Research interests:
Analysis and synthesis of algorithms for inductive inference: classification and model trees. Clustering, Association Rule Discovery, Associative Classification. Applications include Knowledge Discovery in Databases and Data Mining, (multi)Relational Data Mining, Data Stream Mining, Map Interpretation and Spatial Data Mining, Data Mining Query Language.

 

Annalisa Appice

PhD in Computer Science

Associate Professor

Dipartimento di Informatica 
Universitŕ degli Studi di Bari 
Via Orabona 4 
70126 Bari - Italy  

phone: +39 080 5443262 
fax: +39 080 5443269  
email: appice@di.uniba.it 

 

  

  

 


Curriculum Vitae et Studiorum

General Info

Nationality: Italian

Education

 Awards

 

Research Projects

 

Activities

Program Committee Membership of:

International Reviewers Committee Member of:

She acted as a referee of several international journals ( Machine Learning , International Journal of Data Mining, Modelling and Management (IJDMMM), International Journal of Geographical Information Science (IJGIS), Information Sciences, AACM Transactions on Knowledge Discovery from Data, Fundamenta Informaticae, Pattern Recognition Letters , Data and Knowledge Engineering (DKE), Knowledge and Information Systems (KAIS), Journal of Science and Engineering (TKJSE), International Journal of Computers and Applications ...) and conferences.

She has participated to the organization of: Multi-Relational Data Mining (MRDM 2007) co-located with the 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) (as co-chair)

She has served in the organizing committee of:

She has participated as speaker in the tutorial on “Knowledge Mining by Symbolic Data Analysis and the Sodas Sofware” organized by Paola Brito and Edwin Diday in conjunction with IFCS 2006 Conference Data Science and Classification, Ljubljana, Slovenia, July 25 - 29, 2006. She has presented a work on “Dissimilarity and Matching”.

Affiliations

 


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Publications

Annalisa Appice at Google Scholar

Publications listed in DBLP

Publications listed in SCOPUS

Journal papers

Giuseppina Andresini, Annalisa Appice, Francesco Paolo Caforio, Donato Malerba, Gennaro Vessio: ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection. Expert Syst. Appl. 201: 117144 (2022) DOI: https://doi.org/10.1016/j.eswa.2022.117144
Giuseppina Andresini, Annalisa Appice, Daniele Iaia, Donato Malerba, Nicolň Taggio, Antonello Aiello: Leveraging autoencoders in change vector analysis of optical satellite images. J. Intell. Inf. Syst. 58(3): 433-452 (2022) DOI: https://doi.org/10.1007/s10844-021-00670-9
Annalisa Appice, Sergio Escalera, José A. Gámez, Heike Trautmann: Introduction to the special issue of the ECML PKDD 2021 journal track. Mach. Learn. 110(10): 2991-2992 (2021) DOI: https://doi.org/10.1007/s10994-021-06062-y
Annalisa Appice, Sergio Escalera, José A. Gámez, Heike Trautmann: Introduction to the special issue of the ECML PKDD 2021 journal track. Data Min. Knowl. Discov. 35(6): 2540-2541 (2021) DOI: https://doi.org/10.1007/s10618-021-00792-2
Annalisa Appice, Angelo Cannarile, Antonella Falini, Donato Malerba, Francesca Mazzia, Cristiano Tamborrino: Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets. J. Intell. Inf. Syst. 57(3): 423-446 (2021) DOI: https://doi.org/10.1007/s10844-021-00656-7
Giuseppina Andresini, Annalisa Appice, Luca De Rose, Donato Malerba: GAN augmentation to deal with imbalance in imaging-based intrusion detection. Future Gener. Comput. Syst. 123: 108-127 (2021) DOI: https://doi.org/10.1016/j.future.2021.04.017
Giuseppina Andresini, Annalisa Appice, Donato Malerba: Autoencoder-based deep metric learning for network intrusion detection. Inf. Sci. 569: 706-727 (2021) DOI: https://doi.org/10.1016/j.ins.2021.05.016
Giuseppina Andresini, Annalisa Appice, Donato Malerba: Nearest cluster-based intrusion detection through convolutional neural networks. Knowl. Based Syst. 216: 106798 (2021) DOI: https://doi.org/10.1016/j.knosys.2021.106798
Vincenzo Pasquadibisceglie, Annalisa Appice, Giovanna Castellano, Donato Malerba, Giuseppe Modugno: ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs. IEEE Access 8: 184073-184 (2020) DOI: https://doi.org/10.1109/ACCESS.2020.3029323
Annalisa Appice, Pietro Guccione, Emilio Acciaro, Donato Malerba: Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification. Appl. Intell. 50(10): 3179-3200 (2020) DOI: https://doi.org/10.1007/s10489-020-01701-8
Annalisa Appice, Giuseppina Andresini, Donato Malerba: Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection. J. Intell. Inf. Syst. 55(1): 1-26 (2020) DOI: https://doi.org/10.1007/s10844-020-00598-6
Giuseppina Andresini, Annalisa Appice, Nicola Di Mauro, Corrado Loglisci, Donato Malerba: Multi-Channel Deep Feature Learning for Intrusion Detection. IEEE Access 8: 53346-5335 (2020) DOI: https://doi.org/10.1109/ACCESS.2020.2980937
Annalisa Appice, Yulia R. Gel, Iliyan Iliev, Vyacheslav Lyubchich, Donato Malerba: A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico. IEEE Access 8: 52713-5272 (2020) DOI: https://doi.org/10.1109/ACCESS.2020.2980634
Sonja Pravilovic, Annalisa Appice, Donato Malerba: Leveraging correlation across space and time to interpolate geophysical data via CoKriging. International Journal of Geographical Information Science 32(1): 191-212 (2018) DOI: https://doi.org/10.1080/13658816.2017.1381338 accepted version
Annalisa Appice, Corrado Loglisci, Donato Malerba: Active learning via collective inference in network regression problems. Inf. Sci. 460: 293-317 (2018) DOI: https://doi.org/10.1016/j.ins.2018.05.028
Annalisa Appice: Towards mining the organizational structure of a dynamic event scenario. J. Intell. Inf. Syst. 50(1): 165-193 (2018) DOI: https://doi.org/10.1007/s10844-017-0451-x
Sonja Pravilovic, Massimo Bilancia, Annalisa Appice, Donato Malerba: Using multiple time series analysis for geosensor data forecasting. Inf. Sci. 380: 31-52 (2017) DOI: https://doi.org/10.1016/j.ins.2016.11.001
Annalisa Appice, Pietro Guccione, Donato Malerba: A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data. Pattern Recognition 63: 229-245 (2017) DOI: https://doi.org/10.1016/j.patcog.2016.10.010
Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari: Recent advances in mining patterns from complex data. J. Intell. Inf. Syst. 47(1): 1-3 (2016) DOI: https://doi.org/10.1007/s10844-016-0415-6
Annalisa Appice, Pietro Guccione, Donato Malerba: Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system. Machine Learning 103(3): 343-375 (2016) DOI: https://doi.org/10.1007/s10994-016-5559-7
Corrado Loglisci, Annalisa Appice, Donato Malerba: Collective regression for handling autocorrelation of network data in a transductive setting. J. Intell. Inf. Syst. 46(3): 447-472 (2016) DOI: https://doi.org/10.1007/s10844-015-0361-8
Annalisa Appice, Donato Malerba: A Co-Training Strategy for Multiple View Clustering in Process Mining. IEEE Trans. Services Computing 9(6): 832-845 (2016) DOI: https://doi.org/10.1109/TSC.2015.2430327
Annalisa Appice, Anna Ciampi, Donato Malerba: Summarizing numeric spatial data streams by trend cluster discovery. Data Min. Knowl. Discov. 29(1): 84-136 (2015) DOI: https://doi.org/10.1007/s10618-013-0337-7
Pietro Guccione, Luigi Mascolo, Annalisa Appice: Iterative Hyperspectral Image Classification Using Spectral-Spatial Relational Features. IEEE Trans. Geoscience and Remote Sensing 53(7): 3615-3627 (2015) DOI: https://doi.org/10.1109/TGRS.2014.2380475
Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Elio Masciari, Giuseppe Manco: Mining complex patterns. J. Intell. Inf. Syst. 42(2): 179-180 (2014) DOI: https://doi.org/10.1007/s10844-013-0301-4
Annalisa Appice, Donato Malerba: Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering. Data Min. Knowl. Discov. 28(5): 1266-1313 (2014) DOI: https://doi.org/10.1007/s10618-014-0372-z
Annalisa Appice, Pietro Guccione, Donato Malerba, Anna Ciampi: Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. Inf. Sci. 285: 162-180 (2014) DOI: https://doi.org/10.1016/j.ins.2013.12.009
Annalisa Appice, Michelangelo Ceci, Donato Malerba: Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database. Fundam. Inform. 129(3): 193-224 (2014) DOI: https://doi.org/10.3233/FI-2014-969
Annalisa Appice, Anna Ciampi, Donato Malerba, Pietro Guccione: Using trend clusters for spatiotemporal interpolation of missing data in a sensor network. J. Spatial Information Science 6(1): 119-153 (2013) DOI: https://doi.org/10.5311/JOSIS.2013.6.102
Daniela Stojanova, Michelangelo Ceci, Annalisa Appice, Donato Malerba, Saso Dzeroski: Dealing with spatial autocorrelation when learning predictive clustering trees. Ecological Informatics 13: 22-39 (2013) DOI: https://doi.org/10.1016/j.ecoinf.2012.10.006
Daniela Stojanova, Michelangelo Ceci, Annalisa Appice, Saso Dzeroski: Network regression with predictive clustering trees. Data Min. Knowl. Discov. 25(2): 378-413 (2012) DOI: https://doi.org/10.1007/s10618-012-0278-6
Annalisa Appice, Michelangelo Ceci, Antonio Turi, Donato Malerba: A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets. Intell. Data Anal. 15(1): 69-88 (2011) DOI: https://doi.org/10.3233/IDA-2010-0456
Donato Malerba, Michelangelo Ceci, Annalisa Appice: A relational approach to probabilistic classification in a transductive setting. Eng. Appl. of AI 22(1): 109-116 (2009) DOI: https://doi.org/10.1016/j.engappai.2008.04.005
Michelangelo Ceci, Annalisa Appice: Spatial associative classification: propositional vs structural approach. J. Intell. Inf. Syst. 27(3): 191-213 (2006) DOI: https://doi.org/10.1007/s10844-006-9950-x
Annalisa Appice, Claudia d'Amato, Floriana Esposito, Donato Malerba: Classification of symbolic objects: A lazy learning approach. Intell. Data Anal. 10(4): 301-324 (2006) DOI: http://content.iospress.com/articles/intelligent-data-analysis/ida00252
Donato Malerba, Floriana Esposito, Michelangelo Ceci, Annalisa Appice: Top-Down Induction of Model Trees with Regression and Splitting Nodes. IEEE Trans. Pattern Anal. Mach. Intell. 26(5): 612-625 (2004) DOI: https://doi.org/10.1109/TPAMI.2004.1273937
Donato Malerba, Floriana Esposito, Antonietta Lanza, Francesca A. Lisi, Annalisa Appice: Empowering a GIS with inductive learning capabilities: the case of INGENS. Computers, Environment and Urban Systems 27(3): 265-281 (2003) DOI: https://doi.org/10.1016/S0198-9715(02)00024-8
Annalisa Appice, Michelangelo Ceci, Antonietta Lanza, Francesca A. Lisi, Donato Malerba: Discovery of spatial association rules in geo-referenced census data: A relational mining approach. Intell. Data Anal. 7(6): 541-566 (2003) DOI: http://content.iospress.com/articles/intelligent-data-analysis/ida00146

For more information, please do not hesitate to email me.

 

Ph.D Thesis


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Software

 

KDB2000: A KDD support system

ARES: Association Rule Extractor from Spatial data

 SONN: a Symbolic Object K-NN classifier

 MuReNA: a MUlti-RElatioNA data mining system

 CORSO: Custering Of Related Structured Objects

 SUMATRA -TRECI: COUNT-BASED WINDOW TREND CLUSTER discovery: Summarization and Interpolation

 SWOD: SLIDING WINDOW INCREMENTAL TREND CLUSTER discovery: Outlier Detection and Classification

 ICT_TICT: (Time-evolving) Interpolative Clustering Tree Learners

 S2TEC: Spectral and Spatio-relational Transductive Ensemble of Classifiers

 S2COTRAC: Spectral-Spatial Co-Training Algorithm for the Transductive Classification of Hyperspectral Imagery Data

 CVAR: cVAR (Spatio-temporal Cluster-based Vector AutoRegressive model)

 CoSTK: CoSTK (CoKriging based Spatio-Temporal Interpolate)

 CoNeRa: CoNeRa (COllective NEtwork Regression via Active learning)

 SoCRATE: SoCRATE (Spectral-spatial CoRrelation SegmenTation based ClAssifier)

 Kometa KDDE: Software -- Kometa

Courses

PhD Courses

Academic year 2016-2017

Process Mining - Bari Academic Years 16/17

Academic year 2015-2016

Stream Data Mining - Bari Academic Years 15/16

Academic year 2014-2015

Stream Data Mining - Bari Academic Years 14/15

Academic year 2013-2014

Data Mining - Bari Academic Years 13/14

Undergraduate Courses

Academic year 2016-2017

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Track A Academic Years 16/17


Academic year 2015-2016

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Track A Academic Years 15/16

Academic year 2014-2015

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Bari Academic Years 14/15

Academic year 2013-2014

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Bari Academic Years 13/14

Informatics (Informatica) Fisica - Bari Academic Years 13/14



Academic year 2012-2013

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Bari Academic Years 12/13



Academic year 2011-2012

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Bari Academic Years 11/12



Academic year 2010-2011

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Bari Academic Years 10/11



Academic year 2009-2010

Algorithms and Data Structures Lab (Laboratorio di Algoritmi e Strutture Dati) - Bari (TPS) Academic Years 09/10

Programming Languages (Informatica: Linguaggi) - Bari (Laurea Triennale in Fisica) Academic Years 09/10

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Bari Academic Years 09/10



Academic year 2008-2009

Advanced Computer Programming Methods (Metodi Avanzati di Programmazione) Informatica - Brindisi Academic Years 08/09