1,721,047 research outputs found
Towards Automatic Classification of Sheet Music
No full textAutomatic music classification has been of interest since digital data about music became available within the Web. For this task, different automatic classification approaches have been proposed but all existing approaches are based on the analysis of sounds. To the best of our knowledge, there is no automatic solution that considers only the sheet music for classification. Therefore, within the following study, we introduce a machine-learning based approach in order to assign an author to new sheet music. Different features, that best represent the style of a writer has been extracted, and are given in input for training to a kNN algorithm. In addition, the article discusses the results and cases when the classifier fails to assign the right author
Multi-objective Evolutionary Rule and Condition Selection for Designing Fuzzy Rule-based Classifiers
No full textIn this paper, we exploit a multi-objective evolutionary algorithm (MOEA) to generate fuzzy rule-based classifiers (FRBCs) with different trade-offs between classification accuracy and rule base complexity. In order to learn the rule base we employ a rule and condition selection (RCS) approach which aims to select a reduced number of rules from a heuristically generated rule base and concurrently a reduced number of conditions for each selected rule. During the multi-objective evolutionary process, we generate the rule bases of the FRBCs by the RCS approach and concurrently learn the membership function parameters of the linguistic values used in the rules. The MOEA has been tested on fifteen classification benchmarks and compared with a similar technique proposed recently in the literature. We show how the FRBCs generated by our approach can achieve considerable accuracies, despite a low rule base complexity
Exploiting Categorization of Online News for Profiling City Areas
No full textProfiling city areas, in terms of citizens' behaviour and commercial and social activities, is an interesting issue in the context of smart cities, especially considering a real-time streaming context. Several methods have been proposed in the literature, exploiting different data sources. In this paper, we propose an approach to perform profiling of city areas based on articles of local online newspapers, by exploiting information regarding the text as well as metadata such as geo-localization and tags. In particular, we use tags associated with each article for identifying macro-categories through clustering analysis on tags embeddings. Further, we employ a text categorization model based on SVM to label online a new article, represented as Bag-of-Words, with one of such categories. The categorization approach has been integrated into a framework recently proposed by the authors for profiling city areas exploiting different web sources of data: the online newspapers are monitored continuously, thus producing a news stream to be analysed. We show experiments performed on the city of Rome, considering data from 2014 to 2018. We discuss the results obtained by adopting different classifiers and present that the best classifier, namely an SVM, can achieve an accuracy and an f1-score up to 93% and 79%, respectively
An overview of recent distributed algorithms for learning fuzzy models in Big Data classification
Full text linkNowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability
An algorithm based on finite state machines with fuzzy transitions for non-intrusive load disaggregation
No full textDespite the raising awareness in general public on environmental changes and high energy costs, a significant part of energy consumption is still due to an improper use of electrical appliances. Thus, there is a growing interest in developing systems for profiling the use of electrical appliances and suggesting adequate policies for energy saving. In this context, we propose a novel approach to extract the power consumption of a set of appliances from aggregate measurements collected from a smart meter. Our approach employs finite state machines based on fuzzy transitions (FSMFT) and a novel disaggregation algorithm. The FSMFTs are used to coarsely model how each type of appliance works. The disaggregation algorithm exploits a database of FSMFTs for, at each meaningful variation of real and reactive aggregate powers, hypothesizing possible configurations of active appliances. This set of configurations is concurrently managed by the algorithm which, whenever requested, outputs the configuration with the highest confidence with respect to the sequence of detected events. We have successfully tested our approach in an experimental environment in which five appliances have been monitored for 30 minutes
Genetic Training Instance Selection in Multi-Objective Evolutionary Fuzzy Systems: A Co-evolutionary Approach
No full textWhen dealing with datasets that are characterized by a large number of instances, multiobjective evolutionary learning (MOEL) of fuzzy rule-based systems (FRBSs) suffers from high computational costs, mainly because of the fitness evaluation. The use of a reduced set of representative instances in place of the overall training set (TS) would considerably lessen the computational effort. Even though a large number of papers have proposed instance selection approaches, mainly in classification problems, how this selection should be performed, especially in the context of regression, is still an open issue. In this paper, we tackle the instance selection problem in the framework of MOEL of FRBSs through a coevolutionary approach. In the execution of the MOEL, periodically, a single-objective genetic algorithm (SOGA) evolves a population of reduced TSs. The SOGA aims to maximize a purposely defined index which measures how much the Pareto fronts computed by using, respectively, the reduced TS and the overall TS are close to each other: The closer the fronts, the more the reduced TS is representative of the overall TS. During the execution of the MOEL, the rule base and the membership function parameters of the fuzzy sets are concurrently learned by maximizing the accuracy and minimizing the complexity. We tested our approach on 12 large datasets. We adopted reduced TSs composed of 5%, 10%, and 20% of the overall TS. Using nonparametric statistical tests, we verified that with 10% and 20% of the overall TS, the Pareto front approximations that are generated by our coevolutionary approach are comparable with the ones generated by applying the MOEL with the overall TS, although the coevolution allows us to save up to 86.36% of the execution time. In addition, the analysis of the behavior of three representative solutions on the test set highlights that the use of the reduced TSs does not affect the generalization capabilities of the generated FRBSs
Fuzzy hoeffding decision tree for data stream classification
Full text linkData stream mining has recently grown in popularity, thanks to an increasing number of applications which need continuous and fast analysis of streaming data. Such data are generally produced in application domains that require immediate reactions with strict temporal constraints. These particular characteristics make problematic the use of classical machine learning algorithms for mining knowledge from these fast data streams and call for appropriate techniques. In this paper, based on the well-known Hoeffding Decision Tree (HDT) for streaming data classification, we introduce FHDT, a fuzzy HDT that extends HDT with fuzziness, thus making HDT more robust to noisy and vague data. We tested FHDT on three synthetic datasets, usually adopted for analyzing concept drifts in data stream classification, and two real-world datasets, already exploited in some recent researches on fuzzy systems for streaming data. We show that FHDT outperforms HDT, especially in presence of concept drift. Furthermore, FHDT is characterized by a high level of interpretability, thanks to the linguistic rules that can be extracted from it
Bridges and Mediation in Higher Distance Education
No full textThis book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Higher Education Learning Methodologies and Technologies Online, HELMeTO 2020, held in Bari, Italy, September 2020. Due to the COVID-19 pandemic the conference was held online.
The 25 revised full papers and 3 short papers presented were carefully reviewed and selected from a total of 59 submissions. The papers present recent research on challenges of implementing emerging technology solution for online, online learning pedagogical frameworks, facing COVID19 emergency in higher education teaching and learning, online learning technologies in practice, online learning strategies and resources, etc
Artificial Intelligence in beyond 5G and 6G Wireless Networks-Preface
No full textThe workshop Artificial Intelligence in beyond 5G and 6G Wireless Networks (AI6G 2022) was organized in Padua on July 21, 2022, co-located with the IEEE World Congress on Computational Intelligence (WCCI 2022) which is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: the 2022 International Joint Conference on Neural Networks (IJCNN 2022), the 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022), and the 2022 IEEE Congress on Evolutionary Computation (IEEE CEC 2022). These workshop proceedings reflect the objective of the workshop, conceived as a markedly multidisciplinary event, to foster cross-fertilization of ideas among the areas of Artificial Intelligence and Wireless Networks. Researchers and professionals working at the interface between these two fields have contributed to these proceeding by presenting results of their ongoing research
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