This paper reports on the development of a library of decision tree algorithms in Java. The basic model of a decision tree algorithm is presented and then used to justify the design choices and system architecture issues. The library has been designed for flexibility and adaptability. Its basic goal was an open system that could easily embody parts of different conventional as well as new algor...

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This paper investigates the performance of evolutionary algorithms in the optimization aspects of oblique decision tree construction and describes their performance with respect to classification accuracy, tree size, and Pareto-optimality of their solution sets. The performance of the evolutionary algorithms is analyzed and compared to the performance of exhaustive (traditional) decision tree c...

Land capability classification (LCC) of a soil map unit is sought for sustainable use, management and conservation practices. High speed, high precision and simple generating of rules by machine learning algorithms can be utilized to construct pre-defined rules for LCC of soil map units in developing decision support systems for land use planning of an area. Decision tree (DT) is one of the mos...

Searching for a binary partition of attribute domains is an important task in data mining. It is present in both decision tree construction and discretization. The most important advantages of decision tree methods are compactness and clearness of knowledge representation as well as high accuracy of classification. Decision tree algorithms also have some drawbacks. In cases of large data tables...

The majority of the existing algorithms for learning decision trees are greedy—a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, the constructed tree is not globally optimal. Furthermore, the greedy algorithms require a fixed amount of time and are not able to generate a better tree if additional time is available. To overcome this problem, we pr...

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It sho...

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal ...

Using satellite imagery for the study of Earth's resources is attended by manyresearchers. In fact, the various phenomena have different spectral response inelectromagnetic radiation. One major application of satellite data is the classification ofland cover. In recent years, a number of classification algorithms have been developed forclassification of remote sensing data. One of the most nota...

using satellite imagery for the study of earth's resources is attended by manyresearchers. in fact, the various phenomena have different spectral response inelectromagnetic radiation. one major application of satellite data is the classification ofland cover. in recent years, a number of classification algorithms have been developed forclassification of remote sensing data. one of the most nota...