Text categorization is a task of automatically assigning documents to a set of predefined categories. Usually it involves a document representation method and term weighting scheme. This paper proposes a new term weighting scheme called Modified Inverse Document Frequency (MIDF) to improve the performance of text categorization. The document represented in MIDF is trained using the support vect...

The Miettinen & Nurminen (1985) method is often used for constructing confidence intervals of the difference in binomial proportions from stratified 2x2 samples. However, the weighting strategy proposed in their paper requires an iterative procedure to implement, which is nested within another iterative procedure for finding the confidence limits. This paper examines the Cochran-Mantel-Haenszel...

A new method of feature weighting, useful also for feature extraction has been described. It is quite efficient and gives quite accurate results. Weighting algorithm may be used with any kind of learning algorithm. The weighting algorithm with k-nearest neighbors model was used to estimate the best feature base for a given distance measure. Results obtained with this algorithm clearly show its ...

Abstract This paper adopts the concept of random weighting estimation to multi-sensor data fusion. It presents a new random weighting estimation methodology for optimal fusion of multi-dimensional position data. A multi-sensor observation model is constructed for multi-dimensional position. Based on this observation model, a random weighting estimation algorithm is developed to estimate positio...

Combined with weight of samples and kernel function, fuzzy clustering method with generalized entropy is studied. Objective function for fuzzy clustering with generalized entropy based on sample weighting is obtained. Following that, fuzzy clustering algorithm with generalized entropy based on sample weighting is presented. In addition, by introducing kernel into the presented objective functio...

Frequency weighting capabilities are introduced in a recent subspace based frequency domain identiication algorithm 8]. Weighting matrices constructed from the impulse response of weighting lters are used to weight a Hankel matrix prior of deriving the signal subspace by the singular value decomposition. The resulting algorithm is shown to asymp-totically produce models which are frequency weig...

Elicitation methods in decision-making under risk allow us to infer the utilities of outcomes as well as the probability weights from the observed preferences of an individual. An optimally efficient elicitation method is proposed, which takes the inevitable distortion of preferences by random errors into account and minimizes the effect of such errors on the inferred utility and probability we...

This paper presents a new approach to discrete choice analysis for risky prospects. Conventional discrete choice analysis focuses on riskless prospects and does not deal with the scenario where the alternatives that the decision-makers choose from are associated with risk. In this paper, we investigate decisionmakers’ risk perception and choice behaviour in choice experiments when they are faci...

This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are three main themes. The rst is the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is how setting up models with t distributed disturbances leads to weighting patterns which are robust ...

We propose and analyze new fast feature weighting algorithms based on different types of feature ranking. Feature weighting may be much faster than feature selection because there is no need to find cut-threshold in the raking. Presented weighting schemes may be combined with several distance based classifiers like SVM, kNN or RBF network (and not only). Results shows that such method can be su...