Structured documents are usually processed by tree-based document transformers, which transform the document tree representing the structure of the input document into another tree structure. Event-based document transformers, by contrast, recognize the input as a stream of parsing events, i. e. lexical tokens, and process the events one by one in an event-driven manner. Event-based document tr...

We present a new algorithm that follows “divide and conquer” machine learning approach and exhibits a few interesting cognitive properties. The algorithm aims at building the decision tree with only one terminal node per class. Splits of tree nodes are constrained to functions that take identical values (true or false) for every instance within the same class. Appropriate splits are found throu...

In this paper, a new approach to working with missing attribute values in inductive learning algorithms is introduced. Three fundamental issues are studied: the splitting criterion, the allocation of values to missing attribute values, and the prediction of new observations. The formal definition for the splitting criterion is given. This definition takes into account the missing attribute valu...

In test cost-sensitive decision systems, it is difficulty for us to find an optimal attribute set and construct a quality classifier with limited cost. The minimal test cost-sensitive attribute reduction is proposed to address the former problem. However, it is inevitable to remove some good even better attributes in the minimal test cost-sensitive attribute reduction. As a result, the classifi...

This paper presents a new strict, purely functional implementation of attribute grammars. Incremental evaluation is obtained via standard function memoization. Our new implementation of attribute grammars increases the incremental behaviour of the evaluators by both reducing the memoization overhead and increasing their potential incrementallity. We present also an attribute grammar transformat...

To overcome the problem of exponentially increasing protein data, drug discoverers need efficient machine learning techniques to predict the functions of proteins which are responsible for various diseases in human body. The existing decision tree induction methodology C4.5 uses the entropy calculation for best attribute selection. The proposed method develops a new decision tree induction tech...