Valentina Colla

A method for resampling imbalanced datasets in binary classification tasks for real-world problems

The paper presents a novel resampling method for binary classification problems on imbalanced datasets. Imbalanced datasets are frequently found in many industrial applications: for instance, the occurrence of particular product defects, the diagnosis of severe diseases in a series of patients or machine faults are rare events whose detection is of utmost importance. In this paper a new resampling method is proposed combining an oversampling and an undersampling technique. Several tests have been developed aiming at assessing the efficiency of the proposed method. Four classifiers based, respectively, on Support Vector Machine, Decision Tree, labelled Self-Organizing Map and Bayesian Classifiers have been developed and applied for binary classification on the following four datasets: a synthetic dataset, a widely used public dataset and two datasets coming from industrial applications. The results that have been obtained in the tests are presented and discussed in the paper; in particular, the performances that are achieved by the four classifiers through the proposed novel resampling approach have been compared to the ones that are obtained, without any resampling, through a widely applied and well known resampling technique, i.e. the classical SMOTE approach, and through another approach coupling informed SMOTE-based oversampling and informed clustering-based undersampling.

Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic

Abstract: This paper describes a novel binary classification method named LASCUS that can be applied to uneven datasets and sensitive problems such as malfunction detection. Such method aims at filling the gap left by traditional algorithms which have difficulties when coping with unbalanced datasets and are not able to satisfactorily recognize unfrequent patterns. The proposed method is based on the use of a self organizing map (SOM) and of a fuzzy inference system (FIS). The SOM creates a set of clusters to be associated either to frequent or unfrequent situations while the FIS determines such association on the basis of data distribution. The method has been tested on the widely used benchmarking Wisconsin breast cancer database and on two industrial applications. The obtained results, which are discussed in the paper, are encouraging and in line with expectations.

A method for sonar based recognition of walking people

Abstract In this paper we investigate the problem of recognising a person based on the rhythmic features of human walking. The perceptual task is performed using in-air sonar sensors. A linear array sensing head with limited complexity is developed to achieve the stated goal; the method of sensory information processing implemented in the device is a blend of grid based mapping and wavelet based multiresolution analysis. Grid based mapping allows to perform detection and tracking of a moving object at the highest signal-to-noise ratios; wavelet based multiresolution analysis allows us to detect the features that are peculiar to walking people in the range measurement sequence extracted from a single sonar sensor of the sensing head. Experimental tests on a number of moving objects with highly time-varying target strengths are carried out; the results prove the feasibility of the approach in terms of recognition rate and acquisition time. The present study contributes to understanding how in-air sonar sensors behave and interact with complex scatterers such as the human body; also, it offers promise for novel applications of sonar technologies in the field of advanced robotics, where the close interaction between human users and robotic systems is on stage.

Train position and speed estimation using wheel velocity measurements

Abstract In order to improve safety and efficiency in the management of modern railways, several systems for monitoring and control of traffic are being developed. Automatic train protection (ATP) systems command an emergency braking procedure if dangerous situations occur, such as insufficient braking distance to one of the next target positions and target velocities. A novel ATP system named SCMT, to be installed on trains running on Italian railways, is currently being designed. One of the components of SCMT is a module for estimating train speed and positions between fixed balises, which communicate to the on-board system the distance to next targets and the velocity requirements at targets. In this paper algorithms are described for distance to target and velocity estimation, capable of compensating for poor wheel-rail adhesion conditions where conventional odometry algorithms may fail. The algorithms were derived using a variety of methods including neural networks, fuzzy logic and crisp logic. The system was designed and trained using a wide set of experimental data, obtained from test runs carried out with different types of vehicles and conditions (in particular, degraded adhesion conditions were investigated).

Outlier Detection Methods for Industrial Applications

An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several causes. The measurement can be incorrectly observed, recorded or entered into the process computer, the observed datum can come from a different population with respect to the normal situation and thus is correctly measured but represents a rare event. In literature different definitions of outlier exist: the most commonly referred are reported in the following: - “An outlier is an observation that deviates so much from other observations as to arouse suspicions that is was generated by a different mechanism “ (Hawkins, 1980). - “An outlier is an observation (or subset of observations) which appear to be inconsistent with the remainder of the dataset” (Barnet & Lewis, 1994). - “An outlier is an observation that lies outside the overall pattern of a distribution” (Moore and McCabe, 1999). - “Outliers are those data records that do not follow any pattern in an application” (Chen and al., 2002). - “An outlier in a set of data is an observation or a point that is considerably dissimilar or inconsistent with the remainder of the data” (Ramasmawy at al., 2000). Many data mining algorithms try to minimize the influence of outliers for instance on a final model to develop, or to eliminate them in the data pre-processing phase. However, a data miner should be careful when automatically detecting and eliminating outliers because, if the data are correct, their elimination can cause the loss of important hidden information (Kantardzic, 2003). Some data mining applications are focused on outlier detection and they are the essential result of a data-analysis (Sane & Ghatol, 2006). The outlier detection techniques find applications in credit card fraud, network robustness analysis, network intrusion detection, financial applications and marketing (Han & Kamber, 2001). A more exhaustive list of applications that exploit outlier detection is provided below (Hodge, 2004): - Fraud detection: fraudulent applications for credit cards, state benefits or fraudulent usage of credit cards or mobile phones. - Loan application processing: fraudulent applications or potentially problematical customers. - Intrusion detection, such as unauthorized access in computer networks.

General Purpose Input Variables Extraction: A Genetic Algorithm Based Procedure GIVE A GAP

The paper presents an application of genetic algorithms to the problem of input variables selection for the design of neural systems. The basic idea of the proposed method lies in the use of genetic algorithms in order to select the set of variables to be fed to the neural networks. However, the main concept behind this approach is far more general and does not depend on the particular adopted model: it can be used for a wide category of systems, also non-neural, and with a variety of performance indicators. The proposed method has been tested on a simple case study, in order to demonstrate its effectiveness. The results obtained in the processing of experimental data are presented and discussed.

A multivariate fuzzy system applied for outliers detection

The paper presents an application of fuzzy logic to the problem of outliers detection. The overall purpose of the work is to point out anomalous data due different causes through a combination of several traditional methods for outliers detection in multivariate datasets and such combination is achieved through a fuzzy inference system. Moreover, the proposed solutions aims to be automatic and self-adaptive, as some parameters which are required for the combination of the different approaches are automatically evaluated by exploiting the available data, without the need of a-priori assumptions or information on a subset of the available data. The proposed method therefore belongs to the class of the unsupervised outliers detection methods. In order to demonstrate the effectiveness of the developed method, extensive tests have been performed on both a simple case study and a database coming from a real industrial context, where the data have to be filtered before their exploitation for process control purposes. The achieved numerical results are presented and discussed.

A fuzzy inference system applied to defect detection in flat steel production

Recently in many industrial fields the exploitation of vision systems for quality control had a considerable increase, which is mainly due to the technological progress experienced by such systems, that, with respect to the past, made their performance more appealing and more reliable while the associated costs are decreased. The advantages of these kind of systems in terms of savings in human resources and improved quality monitoring have become far more evident, by encouraging their adoption in a wide variety of production cycles. The present paper deals with the elaboration and information extraction from images, that represent portions of the surface of flat steel products, and describes an algorithm for defect detection and classification. The overall classification procedure is composed of a preliminary part that is mostly related to image processing and analysis, which aims at pointing out the defect (independently on the class it belongs to), as well as to the extraction of relevant features of the detected defect; the second part exploits a fuzzy inference system in order to analyze the type of defect and solves a classification problem that presently can be addressed only with the support of a human operator. Fuzzy inference systems are suitable to this application because they are able to mimic and reproduce the human reasoning.

Kinematic control of robots with joint constraints

Kinematic control of robot manipulators requires that joint mechanical limits are taken into account in order to avoid the interruption of the task at hand if joint limits are reached. A novel approach to this problem is presented and compared with state of the are techniques. The proposed control scheme allows to explicitly include in the specification of the task position, velocity and acceleration costraints for the joints. An application to an existing redundant arm is discussed and experimental results are presented.

Thresholded Neural Networks for Sensitive Industrial Classification Tasks

In this paper a novel classification method for real world classification tasks is proposed. The method was designed to overcome the difficulties encountered by traditional methods when coping with those real world problems where the key issue is the detection of particular situations - such as for instance machine faults or anomalies - which in some frameworks are hard to be recognized due to some interacting factors that are analyzed within the paper. The method is described and tested on two industrial problems, which show the goodness of the proposed approach and encourage its use in the industrial environments.