Antonello Rizzi

Adaptive resolution min-max classifiers

A high automation degree is one of the most important features of data driven modeling tools and it should be taken into consideration in classification systems design. In this regard, constructive training algorithms are essential to improve the automation degree of a modeling system. Among neuro-fuzzy classifiers, Simpsons (1992) min-max networks have the advantage of being trained in a constructive way. The use of the hyperbox, as a frame on which different membership functions can be tailored, makes the min-max model a flexible tool. However, the original training algorithm evidences some serious drawbacks, together with a low automation degree. In order to overcome these inconveniences, in this paper two new learning algorithms for fuzzy min-max neural classifiers are proposed: the adaptive resolution classifier (ARC) and its pruning version (PARC). ARC/PARC generates a regularized min-max network by a succession of hyperbox cuts. The generalization capability of ARC/PARC technique mostly depends on the adopted cutting strategy. By using a recursive cutting procedure (R-ARC and R-PARC) it is possible to obtain better results. ARC, PARC, R-ARC, and R-PARC are characterized by a high automation degree and allow to achieve networks with a remarkable generalization capability. Their performances are evaluated through a set of toy problems and real data benchmarks. The paper also proposes a suitable index that can be used for the sensitivity analysis of the classification systems under consideration.

The graph matching problem

In this paper, we propose a survey concerning the state of the art of the graph matching problem, conceived as the most important element in the definition of inductive inference engines in graph-based pattern recognition applications. We review both methodological and algorithmic results, focusing on inexact graph matching procedures. We consider different classes of graphs that are roughly differentiated considering the complexity of the defined labels for both vertices and edges. Emphasis will be given to the understanding of the underlying methodological aspects of each identified research branch. A selection of inexact graph matching algorithms is proposed and synthetically described, aiming at explaining some significant instances of each graph matching methodology mainly considered in the technical literature.

Optimized dissimilarity space embedding for labeled graphs

This paper introduces a new general-purpose classification system able to face automatically a wide range of classification problems for labeled graphs. The proposed graph classifier explicitly embeds the input labeled graphs using the dissimilarity representation framework. We developed a method to optimize the dissimilarity space representation estimating the quadratic Renyi entropy of the underlying distribution of the generated dissimilarity values. The global optimization governing the synthesis of the classifier is implemented using a genetic algorithm and it is carried out by means of two operations that perform prototype selection and extraction on the input set of graphs. During the optimization step, we adopted a suitable objective function which includes the classification accuracy achieved by the whole classification model on a validation set. Experimental evaluations have been conducted on both synthetic and well-known benchmarking datasets, achieving competitive test set classification accuracy results with respect to other state-of-the-art graph embedding based classification systems.

A Granular Computing approach to the design of optimized graph classification systems

Research on Graph-based pattern recognition and Soft Computing systems has attracted many scientists and engineers in several different contexts. This fact is motivated by the reason that graphs are general structures able to encode both topological and semantic information in data. While the data modeling properties of graphs are of indisputable power, there are still different concerns about the best way to compute similarity functions in an effective and efficient manner. To this end, suited transformation procedures are usually conceived to address the well-known Inexact Graph Matching problem in an explicit embedding space. In this paper, we propose two graph embedding algorithms based on the Granular Computing paradigm, which are engineered as key procedures of a general-purpose graph classification system. Tests have been conducted on benchmarking datasets relying on both synthetic and real-world data, achieving competitive results in terms of test set classification accuracy.

Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification

Detecting faults in electrical power grids is of paramount importance, both from the electricity operator and consumer point of view. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all components belonging to the whole infrastructure (e.g., cables and related insulation, transformers, and breakers). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid are collected, such as meteorological information. Designing an efficient recognition model to discriminate faults in real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of dissimilarity measures learning and one-class classification techniques. We provide here an in-depth study related to the available data and to the models based on the proposed one-class classification approach. Furthermore, we perform a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based decision rule.

Refining accuracy of environmental data prediction by MoG neural networks

Abstract The prediction of future values of environmental data sequences is mandatory to the cost-effective management of available resources. Consequently, the possibility to improve the prediction accuracy is a very important goal to be pursued. We propose in the present paper two possible approaches for refining the prediction accuracy on real data sequences. Both these approaches make use of Mixture of Gaussian neural networks for the solution of suitable function approximation problems. The first approach pursues the regularization of the learning process based on the reconstructed state of the context delivering the sequence; the second one is based on the particular chaotic nature of the prediction error.

Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction

In this paper, we propose a novel two-phase methodology based on interval type-2 fuzzy sets (T2FSs) to model the human perceptions of the linguistic terms used to describe the online services satisfaction. In the first phase, a type-1 fuzzy set (T1FS) model of an individual’s perception of the terms used in rating user satisfaction is derived through a decomposition-based procedure. The analysis is carried out by using well-established metrics and results from the Social Sciences context. In the second phase, interval T2FS models of online user satisfaction are calculated using a similarity-based data mining procedure. The procedure selects an essential and informative subset of the initial T1FSs that is used to discard the outliers automatically. Resulting interval T2FSs, which are synthesized based on the selected subset of T1FSs only, exhibit reasonable shapes and interpretability.

Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition

In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.

Genetic optimization of a fuzzy control system for energy flow management in micro-grids

In this paper we present an interesting application of Computational Intelligence techniques for the power demand side and flow management optimization in a microgrid. In particular, we used a Fuzzy Logic Controller (FLC) for Time-of use Cost Management program in the microgrid. FLC can either sell and buy energy from outside the microgrid making use of an aggregate of energy storage capacity realized with lithium ion batteries. According to the hybrid Fuzzy-GA paradigm, the Fuzzy Logic Controller that operates decision making on energy flows is optimized by a Genetic Algorithm. The experimental results show that the proposed control system can manage effectively the energy trade with the main grid on the basis of real time prices.

Prediction of telephone calls load using Echo State Network with exogenous variables

We approach the problem of forecasting the load of incoming calls in a cell of a mobile network using Echo State Networks. With respect to previous approaches to the problem, we consider the inclusion of additional telephone records regarding the activity registered in the cell as exogenous variables, by investigating their usefulness in the forecasting task. Additionally, we analyze different methodologies for training the readout of the network, including two novel variants, namely ν-SVR and an elastic net penalty. Finally, we employ a genetic algorithm for both the tasks of tuning the parameters of the system and for selecting the optimal subset of most informative additional time-series to be considered as external inputs in the forecasting problem. We compare the performances with standard prediction models and we evaluate the results according to the specific properties of the considered time-series.