Our experiment shows that the division of attributes in value trees can either increase or decrease the weight of an attribute. The structural variation of value trees may also change the rank of attributes. We propose that our new ®ndings related to the splitting bias, some other phenomena appearing with attribute weighting in value trees, and the number-of-attribute-levels e€ect in conjoint a...

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication co...

We propose and evaluate two indexing schemes for improving the efficiency of data retrieval in high-dimensional databases that are incomplete. These schemes are novel in that the search keys may contain missing attribute values. The first is a multi-dimensional index structure, called the Bitstring-augmented R-tree (BR-tree), whereas the second comprises a family of multiple one-dimensional one...

In recent years there has been a growing interest in Bayesian Network learning from uncertain data. While many researchers focus on Bayesian Network learning from data with tuple uncertainty, Bayesian Network structure learning from data with attribute uncertainty gets little attention. In this paper we make a clear definition of attribute uncertain data and Bayesian Network Learning problem fr...

This paper investigates a dynamic path-based method for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attribute-value pairs) from paths to form new attributes. Compared with other hypothesis-driven new attribute construction methods, the new idea of this method is that it carries out systematic search with pruning over each path of a tree to...

Formulas from monadic second order (mso) logic with one and two free variables can be used to deene the nodes and edges (respectively) of a graph, in terms of a given graph. Such mso deenable graph transductions play a role in the theory of graph grammars. Here we investigate the special case of trees. The main result is that the mso deenable tree transductions are exactly those tree transducti...

Decision rule acquisition is widely used in data mining and machine learning. In this paper, the limitations of the current approaches to reduct for evaluating decision ability are analyzed deeply. Two concepts, i.e. information entropy and information quantity, and the process of constructing decision tree for acquiring decision rule are introduced. Then, the standard of classical significance...

A new algorithm of attribute reduction based on boolean matrix computation is proposed in this paper. The method compresses the valid information stored in table into a binary tree, at the same time deleting the invalid information and sharing a branch about the same prefix information. Some relative concepts such as local core attributes, local attribute reduction and global core attributes, g...

DHT based peer to peer network system is a general range of query scheme. These schemes can support the range of query without modifying the underlying DHTs and they cannot guarantee to return the query results with bounded delay. The query delay in these schemes depends on both the scale of the system and the size of the query space. This paper, proposed Armada, an efficient range of query pro...

We present a new approach for protecting sensitive data in a relational table (columns: attributes; rows: records). If sensitive data can be inferred by unauthorized users with non-sensitive data, we have the inference problem. We consider inference as correct classi cation and approach it with decision tree methods. As in our previous work, sensitive data are viewed as classes of those test da...