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Information
Special Issues
Advanced Learning Methods for Complex Data
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Advanced Learning Methods for Complex Data

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 October 2018) | Viewed by 15522

Special Issue Editors

Prof. Dr. Maurizio Atzori

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: natural language processing; semantic web
Dr. Barbara Pes

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: data mining and machine learning; high-dimensional data analysis; feature selection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the introduction of process modeling for knowledge discovery, the importance of data mining methods has increased dramatically, making this research area relevant and challenging to extract actionable knowledge from complex data. In recent years, new algorithms and machine learning methods have been experimented with to deal with domains that present multiple challenges including high-dimensionality, heterogeneity of features, and complex relationships between data objects.

Emerging approaches are showing the enormous benefits of learning from complex data, including text, video, audio and the large amount of information related to new research domains, such as big data, the Internet of things, cloud computing, etc., often available on the web according to multiple modalities, multiple resources and multiple formats. Many efforts in the machine learning community have been focused on these specialized types of data.

This Special Issue welcomes papers covering a wide range of topics in the area of learning from complex data, including the following areas of interest:

  • Algorithms for advanced data analysis
  • Data mining and knowledge discovery over complex data
  • Platforms and data mining applications in all domains including social, web, bioinformatics and finance
  • Text mining and natural language processing
  • Machine learning and statistical methods for multimedia and graph data
  • Learning methods for data streams and the Internet of things
  • Big data analytics

We accept both research papers and case studies based on robust and strict methodology with a substantial proportion of original (not published elsewhere) content.

Prof. Maurizio Atzori
Prof. Barbara Pes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine Learning
  • Data Mining
  • Text Mining
  • Statistical methods
  • Big data

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Published Papers (4 papers)

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Editorial

Jump to: Research

2 pages, 165 KiB  
Editorial
Special Issue on Advanced Learning Methods for Complex Data
by Maurizio Atzori and Barbara Pes
Information 2019, 10(1), 8; https://doi.org/10.3390/info10010008 - 27 Dec 2018
Cited by 1 | Viewed by 2868
Abstract
The importance of data mining methods has increased dramatically in recent years, making this research area relevant and challenging to extract actionable knowledge from complex data. Indeed, new algorithms and machine learning methods are constantly being explored to deal with domains that present [...] Read more.
The importance of data mining methods has increased dramatically in recent years, making this research area relevant and challenging to extract actionable knowledge from complex data. Indeed, new algorithms and machine learning methods are constantly being explored to deal with domains that present multiple challenges including high-dimensionality, heterogeneity of features, and complex relationships between data objects. This special issue aims at discussing emerging approaches for learning from complex data, including text data, images, and social media data. Full article
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)

Research

Jump to: Editorial

17 pages, 597 KiB  
Article
Evaluating User Behaviour in a Cooperative Environment
by Enrico Bazzi, Nunziato Cassavia, Davide Chiggiato, Elio Masciari, Domenico Saccà, Alessandra Spada and Irina Trubitsyna
Information 2018, 9(12), 303; https://doi.org/10.3390/info9120303 - 30 Nov 2018
Cited by 4 | Viewed by 3361
Abstract
Big Data, as a new paradigm, has forced both researchers and industries to rethink data management techniques which has become inadequate in many contexts. Indeed, we deal everyday with huge amounts of collected data about user suggestions and searches. These data require new [...] Read more.
Big Data, as a new paradigm, has forced both researchers and industries to rethink data management techniques which has become inadequate in many contexts. Indeed, we deal everyday with huge amounts of collected data about user suggestions and searches. These data require new advanced analysis strategies to be devised in order to profitably leverage this information. Moreover, due to the heterogeneous and fast changing nature of these data, we need to leverage new data storage and management tools to effectively store them. In this paper, we analyze the effect of user searches and suggestions and try to understand how much they influence a user’s social environment. This task is crucial to perform efficient identification of the users that are able to spread their influence across the network. Gathering information about user preferences is a key activity in several scenarios like tourism promotion, personalized marketing, and entertainment suggestions. We show the application of our approach for a huge research project named D-ALL that stands for Data Alliance. In fact, we tried to assess the reaction of users in a competitive environment when they were invited to judge each other. Our results show that the users tend to conform to each other when no tangible rewards are provided while they try to reduce other users’ ratings when it affects getting a tangible prize. Full article
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)
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13 pages, 3467 KiB  
Article
Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification
by Andrea Apicella, Anna Corazza, Francesco Isgrò and Giuseppe Vettigli
Information 2018, 9(10), 252; https://doi.org/10.3390/info9100252 - 12 Oct 2018
Cited by 6 | Viewed by 3603
Abstract
The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular [...] Read more.
The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision. Full article
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)
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15 pages, 1314 KiB  
Article
Aspect Term Extraction Based on MFE-CRF
by Yanmin Xiang, Hongye He and Jin Zheng
Information 2018, 9(8), 198; https://doi.org/10.3390/info9080198 - 3 Aug 2018
Cited by 16 | Viewed by 4864
Abstract
This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model [...] Read more.
This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex. Full article
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)
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