. While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, X-of-N. An X-of-N representation is a set containing one or more attribute-value pairs. For a given instance, the value of an X-of-N representation corresponds to the number of its attribute-value pairs that are true of the instance. A single X-of-N representation can directly and simply represent any concept that can be represented by a single conjunctive, a single disjunctive, or a single M-of-N representation commonly used for constructive induction, and the reverse is not true. In this paper, we describe a constructive decision tree learning algorithm, called XofN. When building decision trees, this algorithm creates one X-of-N representation, either as a nominal attribute or as a numeric attribute, at each decision node. The construction of X-of-N representations is carrie...

This paper investigates a novel attribute construction method for decision tree

This paper discusses a dynamic path-based mathod for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attributevalue pairs) from paths to form new attributes. CAT, a constructive decision tree learning algorithm, which adopts this dynamic path-based method is described. It employs the hypothesis-driven strategy for constructing new attributes, and uses conjunction and negation (implicitly) as its constructive operators. Compared with other hypothesis-driven constructive decision tree learning algorithms such as algorithms of the Fringe family, the new idea of CAT is that it carries out systematic search with pruning over each path of a tree to select conditions for generating a conjunction. Therefore, in CAT, conditions for constructing new attributes are decided dynamically during search. Empirically investigation in a set of artificial and real-world domains shows that CAT can improve the performance of selective decision tree learning in terms of both higher prediction accuracy and lower theory complexity. In addition, it shows some performance advantages over the constructive decision tree learning algorithms that use a fixed path-based method and a fixed rule-based method to construct conjunctions as new attributes. 1 Introduction

In this paper we discuss the problem of selecting appropriate operators for constructive induction. We argue that in problems that are described, at least partially, by continuous features, discriminant analysis is a useful tool for constructive induction. This new method for constructive induction was implemented in system Ltree. Ltree is a data driven constructive induction system, able to define decision surfaces both orthogonal and oblique to the axes defined by the attributes of the input space. This is done by combining a decision tree with a linear discriminant by means of constructive induction. At each decision node, Ltree defines a new instance space by the insertion of new attributes that are projections of the examples that fall at this node over the hyper-planes, given by a linear discriminant function. This new instance space is propagated down through the tree. Tests based on those new attributes are oblique with respect to the original input space. Ltree is a probabil...

Feature manipulation refers to the process by which the input space of a machine learning task is altered in order to improve the learning quality and performance. Three major aspects of feature manipulation are feature construction, feature ranking and feature selection. This thesis proposes a new filter-based methodology for feature manipulation in classification problems using genetic programming (GP). The goal is to modify the input representation of classification problems in order to improve classification performance and reduce the complexity of classification models. The thesis regards classification problems as a collection of variables including conditional variables (input features) and decision variables (target class labels). GP is used to discover the relationships between these variables. The types of relationship and the ways in which they are discovered vary with the three aspects of feature manipulation. In feature construction, the thesis proposes a GP-based method to construct high-level features in the form of functions of original input features.

Abstract — Data mining is the process of finding new patterns. Classification is the technique of generalizing known structure to apply to new data. Classification using a decision tree is performed by routing from the root node until arriving at a leaf node. To model classification process, decision tree is used. Decision can handle both continuous and categorical data. In this research work, Comparison is made between ID3, C4.5 and C5.0. Among these classifiers C5.0 gives more accurate and efficient output with comparatively high speed. Memory usage to store the ruleset in case of the C5.0 classifier is less as it generates smaller decision tree. This research work supports high accuracy, good speed and low memory usage as proposed system is using C5.0 as the base classifier. The classification process here has