Remainder subset awareness for feature subset selection

Feature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. The family of algorithms known as plus-l-minus-r and its immediate derivatives (like forward selection) are very popular and often the only viable alternative when used in wrapper mode. In consequence, it is of the greatest importance to take the most of every evaluation of the inducer, which is normally the more costly part. In this paper, a technique is proposed that takes into account the inducer evaluation both in the current subset and in the remainder subset (its complementary set) and is applicable to any sequential subset selection algorithm at a reasonable overhead in cost. Its feasibility is demonstrated on a series of benchmark data sets.Peer ReviewedPostprint (published version

DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout

The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20\%-50\% of the inputs are not available

Using Twitter to learn about the autism community

Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals' carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD -- their behaviour, concerns, needs etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.Comment: Social Network Analysis and Mining, 201

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Towards more reliable feature evaluations for classification

In this thesis we study feature subset selection and feature weighting algorithms. Our aim is to make their output more stable and more useful when used to train a classifier. We begin by defining the concept of stability and selecting a measure to asses the output of the feature selection process. Then we study different sources of instability and propose modifications of classic algorithms that improve their stability. We propose a modification of wrapper algorithms that take otherwise unused information into account to overcome an intrinsic source of instability for this algorithms: the feature assessment being a random variable that depends on the particular training subsample. Our version accumulates the evaluation results of each feature at each iteration to average out the effect of the randomness. Another novel proposal is to make wrappers evaluate the remainder set of features at each step to overcome another source of instability: randomness of the algorithms themselves. In this case, by evaluating the non-selected set of features, the initial choice of variables is more educated. These modifications do not bring a great amount of computational overhead and deliver better results, both in terms of stability and predictive power. We finally tackle another source of instability: the differential contribution of the instances to feature assessment. We present a framework to combine almost any instance weighting algorithm with any feature weighting one. Our combination of algorithms deliver more stable results for the various feature weighting algorithms we have tested. Finally, we present a deeper integration of instance weighting with feature weighting by modifying the Simba algorithm, that delivers even better results in terms of stabilityEl focus d'aquesta tesi és mesurar, estudiar i millorar l’estabilitat d’algorismes de selecció de subconjunts de variables (SSV) i avaluació de variables (AV) en un context d'aprenentatge supervisat. El propòsit general de la SSV en un context de classificació és millorar la precisió de la predicció. Nosaltres afirmem que hi ha un altre gran repte en SSV i AV: l’estabilitat des resultats. Un cop triada una mesura d’estabilitat entre les estudiades, proposem millores d’un algorisme molt popular: el Relief. Analitzem diferents mesures de distància a més de la original i estudiem l'efecte que tenen sobre la precisió, la detecció de la redundància i l'estabilitat. També posem a prova diferents maneres d’utilitzar els pesos que es calculen a cada pas per influir en el càlcul de distàncies d’una manera similar a com ho fa un altre algorisme d'AV: el Simba. També millorem la seva estabilitat incrementant la contribució dels pesos de les variables en el càlcul de la distància a mesura que avança el temps per minimitzar l’impacte de la selecció aleatòria de les primeres instàncies. Pel què fa als algorismes embolcall, (wrappers) els modifiquem per tenir en compte informació que era ignorada per superar una font intrínseca d’inestabilitat: el fet que l’avaluació de les variables és una variable aleatòria que depèn del subconjunt de dades utilitzat. La nostra versió acumula els resultats en cada iteració per compensar l’efecte aleatori mentre que els originals descarten tota la informació recollida sobre cada variable en una determinada iteració i comencen de nou a la següent, donant lloc a resultats més inestables. Una altra proposta és fer que aquests wrappers avaluïn el subconjunt de variables no seleccionat en cada iteració per evitar una altra font d’inestabilitat. Aquestes modificacions no comporten un gran augment de cost computacional i els seus resultats són més estables i més útils per un classificador. Finalment proposem ponderar la contribució de cada instància en l’AV. Poden existir observacions atípiques que no s'haurien de tenir tant en compte com les altres; si estem intentant predir un càncer utilitzant informació d’anàlisis genètics, hauríem de donar menys credibilitat a les dades obtingudes de persones exposades a grans nivells de radiació tot i que no tenir informació sobre aquesta exposició. Els mètodes d’avaluació d’instàncies (AI) pretenen identificar aquests casos i assignar-los pesos més baixos. Varis autors han treballat en esquemes d’AI per millorar la SSV però no hi ha treball previ en la combinació d'AI amb AV. Presentem un marc de treball per combinar algorismes d'AI amb altres d'AV. A més proposem un nou algorisme d’AI basat en el concepte de marge de decisió que utilitzen alguns algorismes d’AV. Amb aquest marc de treball hem posat a prova les modificacions contra les versions originals utilitzant varis jocs de dades del repositori UCI, de xips d'ADN i els utilitzats en el desafiament de SSV del NIPS-2003. Les nostres combinacions d'algorismes d'avaluació d'instàncies i atributs ens aporten resultats més estables per varis algorismes d'avaluació d'atributs que hem estudiat. Finalment, presentem una integració més profunda de l'avaluació d'instàncies amb l'algorisme de selecció de variables Simba consistent a utilitzar els pesos de les instàncies per ponderar el càlcul de les distàncies, amb la que obtenim resultats encara millors en termes d’estabilitat. Les contribucions principals d’aquesta tesi son: (i) aportar un marc de treball per combinar l'AI amb l’AV, (ii) una revisió de les mesures d’estabilitat de SSV, (iii) diverses modificacions d’algorismes de SSV i AV que milloren la seva estabilitat i el poder predictiu del subconjunt de variables seleccionats; sense un augment significatiu del seu cost computacional, (iv) una definició teòrica de la importància d'una variable i (v) l'estudi de la relació entre l'estabilitat de la SSV i la redundància de les variables.Postprint (published version

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors

Robot awareness of human actions is an essential research problem in robotics with many important real-world applications, including human-robot collaboration and teaming. Over the past few years, depth sensors have become a standard device widely used by intelligent robots for 3D perception, which can also offer human skeletal data in 3D space. Several methods based on skeletal data were designed to enable robot awareness of human actions with satisfactory accuracy. However, previous methods treated all body parts and features equally important, without the capability to identify discriminative body parts and features. In this paper, we propose a novel simultaneous Feature And Body-part Learning (FABL) approach that simultaneously identifies discriminative body parts and features, and efficiently integrates all available information together to enable real-time robot awareness of human behaviors. We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features. We also develop an optimization algorithm to solve the formulated problem, which possesses a theoretical guarantee to find the optimal solution. To evaluate FABL, three experiments were performed using public benchmark datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter robot in practical assistive living applications. Experimental results show that our FABL approach obtains a high recognition accuracy with a processing speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method to enable real-time robot awareness of human behaviors in practical robotics applications.Comment: 8 pages, 6 figures, accepted by ICRA'1

A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset

This paper aims to determine which is the best human action recognition method based on features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the papers that make reference to MSR Action3D, the most used dataset that includes depth information acquired from a RGB-D device, has been performed. We found that the validation method used by each work differs from the others. So, a direct comparison among works cannot be made. However, almost all the works present their results comparing them without taking into account this issue. Therefore, we present different rankings according to the methodology used for the validation in orden to clarify the existing confusion.Comment: 16 pages and 7 table