We present a decision tree evaluation method integrated with a common framework for analyzing multi-attribute decisions under risk, where information is numerically imprecise. The approach extends the use of additive and multiplicative utility functions for supporting evaluation of imprecise statements, relaxing requirements for precise estimates of decision parameters. Information is modeled i...

The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness—the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser d...

Most decision tree classifiers are designed to keep class histograms for single attributes, and to select a particular attribute for the next split using said histograms. In this paper, we propose a technique where, by keeping histograms on attribute pairs, we achieve (i) a significant speed-up over traditional classifiers based on single attribute splitting, and (ii) the ability of building cl...

Finding interesting rule in the sixth strategy step about threshold control on generalized relations in attribute oriented induction, there is possibility to select candidate attribute for further generalization and merging of identical tuples until the number of tuples is no greater than the threshold value, as implemented in basic attribute oriented induction algorithm. At this strategy step ...

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...

Microarray gene dataset often contains high dimensionalities which cause difficulty in clustering and classification. Datasets containing huge number of genes lead to increased complexity and therefore, degradation of dataset handling performance. Often, all the measured features of these high-dimensional datasets are not relevant for understanding the underlying phenomena of interest. Dimensio...

Traditional rough set-based approaches to reduct have difficulties in constructing optimal decision tree, such as empty branches and over-fitting, selected attribute with more values, and increased expense of computational effort. It is necessary to investigate fast and effective search algorithms. In this paper, to address this issue, the limitations of current knowledge reduction for evaluati...

The attributes’ interdependencies have strong effect on understandability of tree based models. If strong dependencies between the attributes are not recognized and these attributes are not used as splits near the root of the tree this causes node replications in lower levels of the tree, blurs the description of dependencies and also might cause drop of accuracy. If Relief family of algorithms...

Generalized cases are cases that cover a subspace rather than a point in the problem-solution space. Attribute dependent generalized cases are a subclass of generalized cases, which cause a high computational complexity during similarity assessment. We present a new approach for an efficient indexbased retrieval of such generalized cases by an improved kdtree approach. The experimental evaluati...

A decision tree is an effective means of data classification from which one can obtain rules that are easy to understand. However, decision trees cannot be conventionally constructed for data which are not explicitly expressed with attribute-value pairs such as graph-structured data. We have proposed a novel algorithm, named Chunkingless Graph-Based Induction (Cl-GBI), for extracting typical pa...