The main goal of the rule space method is to classify examinees into the closest ideal attribute mastery patterns and to assigned appropriate attribute mastery probabilities to them based on their item response patterns. The quality of pattern classification and probability assignment depends on the validity of the proposed attributes and the Qmatrix. The rule-space method provides the quality ...

In this work, we adhere to explore a Multi-Tasking learning (MTL) based network perform document attribute classification such as the font type, size, emphasis and scanning resolution of image. To accomplish these tasks, operate on either segmented word level or uniformed size patches randomly cropped out document. Furthermore, hybrid convolution neural (CNN) architecture ”MTL+MI”, which is com...

Attribute selection is a data preprocessing step which aims at identifying relevant attributes for a target data mining task – specifically in this article, the classification task. Previously, we have proposed a new attribute selection strategy – based on a lazy learning approach – which postpones the identification of relevant attributes until an instance is submitted for classification. Expe...

The basis of many systems that integrate data from multiple sources is a set of correspondences between source schemata and a target schema. Correspondences express a relationship between sets of source attributes, possibly from multiple sources, and a set of target attributes. Clio is an integration tool that assists users in de ning value correspondences between attributes [1]. In real life s...

We develop approximation algorithms for reducing expected classification cost, when there is a cost associated with obtaining the value of each attribute, and we are doing classification based on an ensemble of linear threshold classifiers. We focus on the stochastic setting where attribute values are independent, and their distributions are given. We review related work based on reductions to ...

Classification is an important technology in data mining, while clonal selection algorithm (CSA) is a very effective classification method. Although CSA brings a new effective tool for solving complex problems, we can not completely say that it over-performs to other algorithms especially in the classification field. A main problem of CSA classifier is that it does not carry attribute imbalance...

This paper introduces how to apply rough set classification and attribute reduction method in map generalization and provides solutions to several special cases arising in the map spatial data processing. Unlike the analysis in the past which focused solely on the attribute data of map, in this paper, rough set attribute decision table specific for the features of map data is created on the bas...

The k-Nearest Neighbour (k-NN) method is a typical lazy learning paradigm for solving classification problems. Although this method was originally proposed as a non-parameterised method, attribute weight setting has been commonly adopted to deal with irrelevant attributes. In this paper, we propose a new attribute weight setting method for k-NN based classifiers using quadratic programming, whi...

The naive Bayes (NB) is a popular classification technique for data mining and machine learning, which is based on the attribute independence assumption. Researchers have proposed out many effective methods to improve the performance of NB by lowering its primary weakness---the assumption that attributes are independent given the class, such as backwards sequential elimination method, lazy elim...