1 The authors are with the Department of Computer Science, the University of Liverpool, Liverpool L69 3BX, UK, emails: {lzhang, frans, phl}@csc.liv.ac.uk. Abstract. In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, which is particular suitable for binary classification problems. Our method formalises the attribute weight sett...

In the paper nine different approaches to missing attribute values are presented and compared. Ten input data files were used to investigate the performance of the nine methods to deal with missing attribute values. For testing both naive classification and new classification techniques of LERS (Learning from Examples based on Rough Sets) were used. The quality criterion was the average error r...

Facial attribute analysis in the real world scenario is very challenging mainly because of complex face variations. Existing works of analyzing face attributes are mostly based on the cropped and aligned face images. However, this result in the capability of attribute prediction heavily relies on the preprocessing of face detector. To address this problem, we present a novel jointly learned dee...

This paper studies the effect of synthetic feature vectors on the classification performance of hyperspectral remote sensing images. As feature vectors, it has been chosen to employ morphological attribute profiles, that have proven themselves in this field. At this early stage of our work, the relatively simple Bootstrapping algorithm has been used for synthetic feature vector generation. Base...

In the paper, two families of lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic (with a relation “attribute = value” on the right hand side) and inhibitory (with a relation “attribute 6= value” on the right hand side) rules, but the direct generation of rules is not required. Instead of this, the considered algorithms extrac...

It has been shown that user modelling has the potential to improve the performance of conversational search systems, particularly in what concerns the problem of attribute selection, i.e., determining which attribute to ask the user at each step of the dialogue. In this paper we present a novel framework for attribute selection which allows the fine-tuning of the relative importance of profile-...

This paper explores the use of the naive Bayes classifier as the basis for personalized spam filters. Various machine learning algorithms, including variants of naive Bayes, have previously been used for this purpose, but the author’s implementation using word position based attribute vectors gives very good results when tested on several publicly available corpora. The effect of various forms ...

The goal of our research is to distinguish veterinary message board posts that describe a case involving a specific patient from posts that ask a general question. We create a text classifier that incorporates automatically generated attribute lists for veterinary patients to tackle this problem. Using a small amount of annotated data, we train an information extraction (IE) system to identify ...

For coded SIMO-OFDM systems, preDFT combining was shown to provide a good trade-off between error-rate performance and processing complexity. Max-sum SNR and max-min SNR are two reasonable ways for obtaining these combining weights. In this paper multi attribute augmentation is Employed to further reveal the suitability and limitation of these two criteria. The results show that neither max-sum...

State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level att...