Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Davide, Bacciu; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Alessio, Micheli; Alessandro, Saffiotti An Ambient Intelligence Approach for Learning in Smart Robotic Environments Journal Article In: Computational Intelligence, 2019, (Early View (Online Version of Record before inclusion in an issue)
). Davide, Bacciu Unsupervised feature selection for sensor time-series in pervasive computing applications Journal Article In: Neural Computing and Applications, vol. 27, no. 5, pp. 1077-1091, 2016, ISSN: 1433-3058. Davide, Bacciu; Filippo, Benedetti; Alessio, Micheli ESNigma: efficient feature selection for Echo State Networks Conference Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), i6doc.com publ., 2015. Davide, Bacciu An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications Conference Communications in Computer and Information Science - Engineering Applications of Neural Networks, vol. 459, Springer International Publishing, 2014. Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli; Maurizio, Di Rocco; Alessandro, Saffiotti Learning context-aware mobile robot navigation in home environments Conference Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014), IEEE, 2014, ISBN: 9781479961702. JG, Lisboa Paulo; H, Jarman Ian; A, Etchells Terence; Davide, Bacciu; M, Garibaldi John Model-based and model-free clustering: a case study of protein expression data for breast cancer Conference PROCEEDINGS OF THE 2009 UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE, 2009. Davide, Bacciu; Elia, Biganzoli; JG, Lisboa Paulo; Antonina, Starita Unsupervised Breast Cancer Class Discovery: a Comparative Study on Model-based and Neural Clustering Book Section In: pp. 13-26, KES Rapid Research Results Series, 2008. Davide, Bacciu A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections PhD Thesis 2008. Davide, BACCIU; Elia, BIGANZOLI; JG, LISBOA Paulo; Antonina, Starita Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics Conference Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08), vol. 5178, Springer, 2008. Davide, Bacciu; Alessio, Micheli; Antonina, Starita Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking Technical Report Università di Pisa 2007. Davide, BACCIU; Alessio, MICHELI; Antonina, STARITA Simultaneous clustering and feature ranking by competitive repetition suppression learning with application to gene data analysis Conference Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED 2007), 2007.@article{rubicon2019CI,
title = {An Ambient Intelligence Approach for Learning in Smart Robotic Environments},
author = {Bacciu Davide and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Micheli Alessio and Saffiotti Alessandro},
doi = {10.1111/coin.12233},
year = {2019},
date = {2019-07-31},
journal = {Computational Intelligence},
abstract = {Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. In order to reduce the amount of preparation and pre-programming required for their deployment in real world applications, it is important to make these systems self-learning, self-configuring, and self-adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre-existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to pro-actively and smoothly adapt to subtle changes in the environment and in the habits and preferences
of their user(s).},
note = {Early View (Online Version of Record before inclusion in an issue)
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
of their user(s).
@article{icfNca15,
title = {Unsupervised feature selection for sensor time-series in pervasive computing applications},
author = {Bacciu Davide},
url = {https://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/nca2015.pdf},
doi = {10.1007/s00521-015-1924-x},
issn = {1433-3058},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Neural Computing and Applications},
volume = {27},
number = {5},
pages = {1077-1091},
publisher = {Springer London},
abstract = {The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{11568_774434,
title = {ESNigma: efficient feature selection for Echo State Networks},
author = {Bacciu Davide and Benedetti Filippo and Micheli Alessio},
url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-104.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15)},
pages = {189--194},
publisher = {i6doc.com publ.},
abstract = {The paper introduces a feature selection wrapper designed specifically for Echo State Networks. It defines a feature scoring heuristics, applicable to generic subset search algorithms, which allows to reduce the need for model retraining with respect to wrappers in literature. The experimental assessment on real-word noisy sequential data shows that the proposed method can identify a compact set of relevant, highly predictive features with as little as $60%$ of the time required by the original wrapper.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{icfEann14,
title = {An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications},
author = {Bacciu Davide},
doi = {10.1007/978-3-319-11071-4_4},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Communications in Computer and Information Science - Engineering Applications of Neural Networks},
journal = {COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE},
volume = {459},
pages = {39--48},
publisher = {Springer International Publishing},
abstract = {The paper discusses an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised
to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data
from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data
from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.@conference{11568_588269,
title = {Learning context-aware mobile robot navigation in home environments},
author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Di Rocco Maurizio and Saffiotti Alessandro},
doi = {10.1109/IISA.2014.6878733},
isbn = {9781479961702},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014)},
pages = {57--62},
publisher = {IEEE},
abstract = {We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_466869,
title = {Model-based and model-free clustering: a case study of protein expression data for breast cancer},
author = {Lisboa Paulo JG and Jarman Ian H and Etchells Terence A and Bacciu Davide and Garibaldi John M},
year = {2009},
date = {2009-01-01},
booktitle = {PROCEEDINGS OF THE 2009 UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@incollection{bacciu2010unsupervised,
title = {Unsupervised Breast Cancer Class Discovery: a Comparative Study on Model-based and Neural Clustering},
author = {Bacciu Davide and Biganzoli Elia and Lisboa Paulo JG and Starita Antonina},
year = {2008},
date = {2008-01-01},
pages = {13-26},
publisher = {KES Rapid Research Results Series},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
@phdthesis{11568_466874,
title = {A Perceptual Learning Model to Discover the Hierarchical Latent Structure of Image Collections},
author = { Bacciu Davide},
url = {http://e-theses.imtlucca.it/id/eprint/7},
doi = {10.6092/imtlucca/e-theses/7},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
publisher = {IMT Lucca},
abstract = {Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
@conference{11568_465487,
title = {Are Model-based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics},
author = {BACCIU Davide and BIGANZOLI Elia and LISBOA Paulo JG and Starita Antonina},
doi = {10.1007/978-3-540-85565-1-23},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'08)},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {5178},
pages = {181--188},
publisher = {Springer},
abstract = {A novel neural network clustering algorithm, CoRe, is benchmarked against previously published results on a breast cancer data set and applying the method of Partition Around Medoids (PAM). The data serve to compare the samples partitions obtained with the neural network, PAM and model-based algorithms, namely Gaussian Mixture Model (GMM), Variational Bayesian Gaussian Mixture (VBG) and Variational Bayesian Mixtures with Splitting (VBS). It is found that CoRe, on the one hand, agrees with the previously published partitions; on the other hand, it supports the existence of a supplementary cluster that we hypothesize to be an additional tumor subgroup with respect to those previously identified by PAM},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@techreport{11568_255939,
title = {Feature-wise Competitive Repetition Suppression Learning for Gene Data Clustering and Feature Ranking},
author = {Bacciu Davide and Micheli Alessio and Starita Antonina},
url = {http://compass2.di.unipi.it/TR/Files/TR-07-04.pdf.gz},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
volume = {TR-07-04},
pages = {1--14},
institution = {Università di Pisa},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
@conference{11568_116977,
title = {Simultaneous clustering and feature ranking by competitive repetition suppression learning with application to gene data analysis},
author = {BACCIU Davide and MICHELI Alessio and STARITA Antonina},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED 2007)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}