The determination of ordered weighted averaging (OWA) operator weights is a very important issue of applying the OWA operator for decision making. One of the first approaches, suggested by O’Hagan, determines a special class of OWA operators having maximal entropy of the OWA weights for a given level of orness; algorithmically it is based on the solution of a constrained optimization problem. I...

Partly motivated by entropy-estimation problems in neuroscience, we present a detailed and extensive comparison between some of the most popular and effective entropy estimation methods used in practice: The plug-in method, four different estimators based on the Lempel-Ziv (LZ) family of data compression algorithms, an estimator based on the Context-Tree Weighting (CTW) method, and the renewal ...

1 Knowledge Engineering & Machine Learning Group, Technical University of Catalonia, Barcelona, email: {hnunez, miquel}@lsi.upc.es Abstract. The major hypothesis that we will be prove in this paper is that unsupervised learning techniques of feature weighting are not significantly worse than supervised methods, as is commonly believed in the machine learning community. This paper tests the powe...

The entropy rate quantifies the amount of uncertainty or disorder produced by any dynamical system. In a spiking neuron, this uncertainty translates into the amount of information potentially encoded and thus the subject of intense theoretical and experimental investigation. Estimating this quantity in observed, experimental data is difficult and requires a judicious selection of probabilistic ...

Recently the research on supervised term weighting has attracted growing attention in the field of Traditional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show that existing methods more or less suffer from the problem of over-weighting. Overlooked by prior studies, over-weighting is a new concept proposed in this paper. To address this probl...

We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illu...

In this paper, we present an entropy based method to combine tandem representations of the recently proposed Phase AutoCorrelation (PAC) based features and MelFrequency Cepstral Coefficients (MFCC) features. PAC based features, derived from a nonlinear transformation of autocorrelation coefficients and shown to be noise robust, improve their robustness to additive noise in their tandem represen...

Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM which are methods to systematically evaluate alternatives, we propose a new Fuzzy TOPSIS for evaluating outsourcing software companies both using subjective and objective weights. While crisp data are inadequate to m...

In this paper, we consider identifying codes in binary Hamming spaces F, i.e., in binary hypercubes. The concept of identifying codes was introduced by Karpovsky, Chakrabarty and Levitin in 1998. Currently, the subject forms a topic of its own with several possible applications, for example, to sensor networks. Let C ⊆ F. For any X ⊆ F, denote by Ir(X) = Ir(C;X) the set of elements of C within ...

Let (X, d) be a compact metric space, f : X 7→ X be a continuous map with the specification property, and φ : X 7→ R a continuous function. We consider the set of points for which the Birkhoff average of φ does not exist (which we call the irregular set for φ) and show that this set is either empty or carries full topological pressure (in the sense of Pesin and Pitskel). We formulate various eq...