The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.

Compact Reconfigurable Binary-Decision-Diagram Logic Circuit on a GaAs Nanowire Network

We describe a reconfigurable binary-decision-diagram logic circuit based on Shannon's expansion of Boolean logic function and its graphical representation on a semiconductor nanowire network. The circuit is reconfigured by using programmable switches that electrically connect and disconnect a small number of branches. This circuit has a compact structure with a small number of devices compared with the conventional look-up table architecture. A variable Boolean logic circuit was fabricated on an etched GaAs nanowire network having hexagonal topology with Schottky wrap gates and SiN-based programmable switches, and its correct logic operation together with dynamic reconfiguration was demonstrated.

An automata theoretic decision procedure for the propositional mu-calculus

The propositional mu-calculus is a propositional logic of programs which incorporates a least fixpoint operator and subsumes the propositional dynamic logic of Fischer and Ladner, the infinite looping construct of Streett, and the game logic of Parikh. We give an elementary time decision procedure, using a reduction to the emptiness problem for automata on infinite trees. A small model theorem is obtained as a corollary.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.