A comparison of fuzzy approaches for training a humanoid robotic football player

© 2017 IEEE. Fuzzy Systems are an efficient instrument to create efficient and transparent models of the behavior of complex dynamic systems such as autonomous humanoid robots. The human interpretability of these models is particularly significant when it is applied to the cognitive robotics research, in which the models are designed to study the behaviors and produce a better understanding of the underlying processes of the cognitive development. From this research point of view, this paper presents a comparative study on training fuzzy based system to control the autonomous navigation and task execution of a humanoid robot controlled in a soccer scenario. Examples of sensor data are collected via a computer simulation, then we compare the performance of several fuzzy algorithms able to learn and optimize the humanoid robot's actions from the data

Real-Time Classification of Twitter Trends

Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons. In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with following four types: 'news', 'ongoing events', 'memes', and 'commemoratives'. While previous research has analyzed trending topics in a long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This would allow to provide a filtered subset of trends to end users. We analyze and experiment with a set of straightforward language-independent features based on the social spread of trends to categorize them into the introduced typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might enrich marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.Comment: Pre-print of article accepted for publication in Journal of the American Society for Information Science and Technology copyright @ 2013 (American Society for Information Science and Technology

Two Information-Theoretic Tools to Assess the Performance of Multi-class Classifiers

We develop two tools to analyze the behavior of multiple-class, or multi-class, classifiers by means of entropic measures on their confusion matrix or contingency table. First we obtain a balance equation on the entropies that captures interesting properties of the classifier. Second, by normalizing this balance equation we first obtain a 2-simplex in a three-dimensional entropy space and then the de Finetti entropy diagram or entropy triangle. We also give examples of the assessment of classifiers with these tools.Spanish Government-Comisión Interministerial de Ciencia y Tecnología projects 2008-06382/TEC and 2008-02473/TEC and the regional projects S-505/TIC/0223 (DGUI-CM) and CCG08-UC3M/TIC-4457 (Comunidad Autónoma de Madrid – UC3M)Publicad

100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox

The most widely spread measure of performance, accuracy, suffers from a paradox: predictive models with a given level of accuracy may have greater predictive power than models with higher accuracy. Despite optimizing classification error rate, high accuracy models may fail to capture crucial information transfer in the classification task. We present evidence of this behavior by means of a combinatorial analysis where every possible contingency matrix of 2, 3 and 4 classes classifiers are depicted on the entropy triangle, a more reliable information-theoretic tool for classification assessment. Motivated by this, we develop from first principles a measure of classification performance that takes into consideration the information learned by classifiers. We are then able to obtain the entropy-modulated accuracy (EMA), a pessimistic estimate of the expected accuracy with the influence of the input distribution factored out, and the normalized information transfer factor (NIT), a measure of how efficient is the transmission of information from the input to the output set of classes. The EMA is a more natural measure of classification performance than accuracy when the heuristic to maximize is the transfer of information through the classifier instead of classification error count. The NIT factor measures the effectiveness of the learning process in classifiers and also makes it harder for them to "cheat" using techniques like specialization, while also promoting the interpretability of results. Their use is demonstrated in a mind reading task competition that aims at decoding the identity of a video stimulus based on magnetoencephalography recordings. We show how the EMA and the NIT factor reject rankings based in accuracy, choosing more meaningful and interpretable classifiers.Francisco José Valverde-Albacete has been partially supported by EU FP7 project LiMoSINe (contract 288024): www.limosine-project.eu Carmen Peláez Moreno has been partially supported by the Spanish Government-Comisión Interministerial de Ciencia y Tecnología project TEC2011–26807

Basic daily activity recognition with a data glove

Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (~220$) and efficient (~100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it

Undirected cyclic graph based multiclass pair-wise classifier: Classifier number reduction maintaining accuracy

Supervised Classification approaches try to classify correctly the new unlabelled examples based on a set of well-labelled samples. Nevertheless, some classification methods were formulated for binary classification problems and has difficulties for multi-class problems. Binarization strategies decompose the original multi-class dataset into multiple two-class subsets. For each new sub-problem a classifier is constructed. One-vs-One is a popular decomposition strategy that in each sub-problem discriminates the cases that belong to a pair of classes, ignoring the remaining ones. One of its drawbacks is that it creates a large number of classifiers, and some of them are irrelevant. In order to reduce the number of classifiers, in this paper we propose a new method called Decision Undirected Cyclic Graph. Instead of making the comparisons of all the pair of classes, each class is compared only with other two classes; evolutionary computation is used in the proposed approach in order to obtain suitable class pairing. In order to empirically show the performance of the proposed approach, a set of experiments over four popular Machine Learning algorithms are carried out, where our new method is compared with other well-known decomposition strategies of the literature obtaining promising results.The authors gratefully acknowledge J. Ceberio for his assistance during the work. The work described in this paper was partially conducted within the Basque Government Research Team Grant IT313-10. I. Mendialdua holds a Grant from Basque Government

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.Funding for open access charge: Universidad de Granada / CBUA.ERDF and the Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge and Universities (grant numbers P18-RT-2248 and B-CTS-536-UGR20)ERDF and Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities (grant number PI20/00711)Spanish Ministry of Science and Innovation (grant number PID2019-107793GB-I00

Optimized limited memory and warping LCSS for online gesture recognition or overlearning?

In this paper, we present and evaluate a new algorithm for online gesture recognition in noisy streams. This technique relies upon the proposed LM-WLCSS (Limited Memory and Warping LCSS) algorithm that has demonstrated its efficiency on gesture recognition. This new method involves a quantization step (via the KMeans clustering algorithm). This transforms new data to a finite set. In this way, each new sample can be compared to several templates (one per class) and gestures are rejected based on a previously trained rejection threshold. Then, an algorithm, called SearchMax, find a local maximum within a sliding window and output whether or not the gesture has been recognized. In order to resolve conflicts that may occur, another classifier could be completed. As the K-Means clustering algorithm, needs to be initialized with the number of clusters to create, we also introduce a straightforward optimization process. Such an operation also optimizes the window size for the SearchMax algorithm. In order to demonstrate the robustness of our algorithm, an experiment has been performed over two different data sets. However, results on tested data sets are only accurate when training data are used as test data. This may be due to the fact that the method is in an overlearning state

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context