A. Appice

D. Malerba, A. Appice, Giuseppina Andresini, 2022, 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech).

Stepwise Induction of Logistic Model Trees

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2008, ISMIS.

Spatial associative classification: propositional vs structural approach

Michelangelo Ceci, Annalisa Appice, A. Appice, 2006, Journal of Intelligent Information Systems.

Propositionalization Through Relational Association Rules Mining

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2005, SEBD.

An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2009, Discovery Science.

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico

Donato Malerba, Annalisa Appice, Yulia R. Gel, 2020, IEEE Access.

Introduction to the special issue of the ECML PKDD 2021 journal track

Sergio Escalera, Heike Trautmann, Annalisa Appice, 2021, Data mining and knowledge discovery.

Introduction to the special issue of the ECML PKDD 2021 journal track

Sergio Escalera, Heike Trautmann, Annalisa Appice, 2021, Data mining and knowledge discovery.

Process Mining to Forecast the Future of Running Cases

Donato Malerba, Sonja Pravilovic, Annalisa Appice, 2013, NFMCP.

A relational approach to probabilistic classification in a transductive setting

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2009, Eng. Appl. Artif. Intell..

Spatial Clustering of Structured Objects

Donato Malerba, Annalisa Appice, Antonietta Lanza, 2005, ILP.

KDB2000: An integrated knowledge discovery tool

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2002 .

GAN augmentation to deal with imbalance in imaging-based intrusion detection

Donato Malerba, Annalisa Appice, Giuseppina Andresini, 2021, Future Gener. Comput. Syst..

Advanced Programming of Intelligent Social Robots

Donato Malerba, Giovanna Castellano, Annalisa Appice, 2019 .

Saliency Detection for Hyperspectral Images via Sparse-Non Negative-Matrix-Factorization and novel Distance Measures*

Donato Malerba, Annalisa Appice, Francesca Mazzia, 2020, 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images

Donato Malerba, Annalisa Appice, Francesca Mazzia, 2020, LOD.

Leveraging correlation across space and time to interpolate geophysical data via CoKriging

Donato Malerba, Sonja Pravilovic, Annalisa Appice, 2018, Int. J. Geogr. Inf. Sci..

Active learning via collective inference in network regression problems

Corrado Loglisci, Donato Malerba, Annalisa Appice, 2018, Inf. Sci..

Application of Spatio-Temporal Clustering in Forecasting Optimization of Geo-Referenced Time Series

A. Appice, Sonja Pravilovic, 2014 .

Collective regression for handling autocorrelation of network data in a transductive setting

Corrado Loglisci, Donato Malerba, Annalisa Appice, 2015, Journal of Intelligent Information Systems.

Iterative Hyperspectral Image Classification Using Spectral–Spatial Relational Features

Annalisa Appice, Pietro Guccione, Luigi Mascolo, 2015, IEEE Transactions on Geoscience and Remote Sensing.

Relational Frequent Patterns Mining for Novelty Detection from Data Streams

Michelangelo Ceci, Corrado Loglisci, Donato Malerba, 2009, MLDM.

Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering

D. Malerba, A. Appice, 2014, Data Mining and Knowledge Discovery.

Stepwise Induction of Multi-target Model Trees

Saso Dzeroski, Annalisa Appice, S. Džeroski, 2007, ECML.

Sampling Training Data for Accurate Hyperspectral Image Classification via Tree-Based Spatial Clustering

Donato Malerba, Sonja Pravilovic, Annalisa Appice, 2017, AI*IA.

Exploiting Spatial Correlation of Spectral Signature for Training Data Selection in Hyperspectral Image Classification

Annalisa Appice, Pietro Guccione, A. Appice, 2016, DS.

Collective regression for handling autocorrelation of network data in a transductive setting

D. Malerba, A. Appice, Corrado Loglisci, 2016, Journal of Intelligent Information Systems.

Enhancing Regression Models with Spatio-temporal Indicator Additions

Donato Malerba, Sonja Pravilovic, Annalisa Appice, 2013, AI*IA.

Learning and Transferring Geographically Weighted Regression Trees across Time

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2011, MSM/MUSE.

Summarizing numeric spatial data streams by trend cluster discovery

D. Malerba, A. Appice, A. Ciampi, 2013, Data Mining and Knowledge Discovery.

Recent advances in mining patterns from complex data

G. Manco, A. Appice, E. Masciari, 2016, Journal of Intelligent Information Systems.

Leveraging autoencoders in change vector analysis of optical satellite images

Donato Malerba, Annalisa Appice, Giuseppina Andresini, 2021, Journal of Intelligent Information Systems.

A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2011, Intell. Data Anal..

Mr-SBC: A Multi-relational Naïve Bayes Classifier

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2003, PKDD.

Empowering a GIS with inductive learning capabilities: the case of INGENS

Donato Malerba, Francesca A. Lisi, Floriana Esposito, 2003, Comput. Environ. Urban Syst..

Autoencoder-based deep metric learning for network intrusion detection

Donato Malerba, Annalisa Appice, Giuseppina Andresini, 2021, Inf. Sci..

Classification of symbolic objects: A lazy learning approach

Donato Malerba, Annalisa Appice, Claudia d'Amato, 2006, Intell. Data Anal..

Geodata Stream Summarization

Donato Malerba, Annalisa Appice, Anna Ciampi, 2014 .

Leveraging the Power of Spatial Data Mining to Enhance the Applicability of GIS Technology

Donato Malerba, Annalisa Appice, Antonietta Lanza, 2009 .

Transductive Learning for Spatial Data Classification

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2010, Advances in Machine Learning I.

A Data Mining Query Language for Knowledge Discovery in a Geographical Information System

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2004, Database Support for Data Mining Applications.

SDMOQL: An OQL-based Data Mining Query Language for Map Interpretation Tasks

Donato Malerba, Annalisa Appice, Nicola Vacca, 2002 .

Foundations of Intelligent Systems

Andrzej Skowron, Dominik Ślęzak, Annalisa Appice, 2017, Lecture Notes in Computer Science.

PROMISE: Coupling predictive process mining to process discovery

A. Appice, G. Castellano, Vincenzo Pasquadibisceglie, 2022, Inf. Sci..

DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring

D. Malerba, A. Appice, G. Castellano, 2023, Engineering Applications of Artificial Intelligence.

Using Convolutional Neural Networks for Predictive Process Analytics

Donato Malerba, Giovanna Castellano, Annalisa Appice, 2019, 2019 International Conference on Process Mining (ICPM).

A Network Intrusion Detection System for Concept Drifting Network Traffic Data

Corrado Loglisci, Donato Malerba, Annalisa Appice, 2021, DS.

Improving Cyber-Threat Detection by Moving the Boundary Around the Normal Samples

Donato Malerba, Annalisa Appice, Giuseppina Andresini, 2020, Studies in Computational Intelligence.

Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Michelangelo Ceci, Donato Malerba, Saso Dzeroski, 2011, Discovery Science.

Generating Logic Descriptions for the Automated Interpretation of Topographic Maps

Michelangelo Ceci, Donato Malerba, Francesca A. Lisi, 2001, GREC.

Collective Inference for Handling Autocorrelation in Network Regression

Corrado Loglisci, Donato Malerba, Annalisa Appice, 2014, ISMIS.

A Multi-View Deep Learning Approach for Predictive Business Process Monitoring

Donato Malerba, Giovanna Castellano, Annalisa Appice, 2021, IEEE Transactions on Services Computing.

Editorial: AI meets cybersecurity

A. Appice, Giuseppina Andresini, 2022, Journal of Intelligent Information Systems.

Activity Prediction of Business Process Instances with Inception CNN Models

Annalisa Appice, Nicola Di Mauro, Teresa M. A. Basile, 2019, AI*IA.

Sensor Networks and Data Streams: Basics

Donato Malerba, Annalisa Appice, Anna Ciampi, 2014 .

Multi-Channel Deep Feature Learning for Intrusion Detection

Corrado Loglisci, Donato Malerba, Annalisa Appice, 2020, IEEE Access.

Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification

Donato Malerba, Annalisa Appice, Pietro Guccione, 2020, Applied Intelligence.

FOX: a neuro-Fuzzy model for process Outcome prediction and eXplanation

Donato Malerba, Giovanna Castellano, Annalisa Appice, 2021, 2021 3rd International Conference on Process Mining (ICPM).

Transductive learning for spatial regression with co-training

Michelangelo Ceci, Donato Malerba, Annalisa Appice, 2010, SAC '10.

Transductive Relational Classification in the Co-training Paradigm

Herna L. Viktor, Michelangelo Ceci, Donato Malerba, 2012, MLDM.

Novelty Detection from Evolving Complex Data Streams with Time Windows

Michelangelo Ceci, Corrado Loglisci, Donato Malerba, 2009, ISMIS.

Anomaly detection in aerospace product manufacturing: Initial remarks

Donato Malerba, Annalisa Appice, Angelo Corallo, 2016, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

Predictive Regional Trees to Supplement Geo-Physical Random Fields

Donato Malerba, Sonja Pravilovic, Annalisa Appice, 2013, CORES.

Discovering process models through relational disjunctive patterns mining

Michelangelo Ceci, Corrado Loglisci, Donato Malerba, 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

$\mathsf{SILVIA}$: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images

D. Malerba, A. Appice, Giuseppina Andresini, 2023, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection

D. Malerba, A. Appice, Giuseppina Andresini, 2022, Expert Syst. Appl..

Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

Donato Malerba, Annalisa Appice, Francesca Mazzia, 2021, Journal of Intelligent Information Systems.

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

D. Malerba, A. Appice, Muhammad Imran, 2024, Future Internet.

TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments

D. Malerba, A. Appice, Vincenzo Pasquadibisceglie, 2023, Journal of Intelligent Information Systems.

Discovering Informative Syntactic Relationships between Named Entities in Biomedical Literature

Michelangelo Ceci, Corrado Loglisci, Annalisa Appice, 2010, 2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications.

A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data

D. Malerba, A. Appice, Giuseppina Andresini, 2024, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.