Inductive learning is the learning that is based on induction. In inductive learningDecision tree algorithms are very famous. For the appropriate classification of the objects with the given attributes inductive methods use these algorithms basically. These algorithms are very important in the classification of the objects. That is why many of these algorithms are used in the intelligent system...

The purpose of this research is to predict the failure of devices using a data mining tool. For this purpose, at the outset, an appropriate database consists of 392 records of ongoing failures in a pharmaceutical company in 1394, in the next step, by analyzing 9 characteristics and type of failure as a database class, analyzes have been used. In this regard, three decision tree algorithms have ...

Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learningbased Decision Trees (RLDT), that uses Reinforcement Learning (RL) to actively examine a minimal number of features of a data point to classify it with high accuracy. Furthermore, RLDT optimizes a long term return, providin...

Decision tree induction algorithms generally adopt a greedy approach to select attributes in order to optimize some criteria at each iteration of the tree induction process. When a decision tree has been constructed, a set of decision rules may be correspondingly derived. Univariate decision tree induction algorithms generally yield the same tree regardless of how many times it is induced from ...

With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed, heterogeneous and autonomous data repositories. In many applications, practical constraints require such systems to provide support for data analysis where the data and the computational resources are available. This ...

To address the contextual bandit problem, we propose online decision tree algorithms. The analysis of proposed algorithms is based on the sample complexity needed to find the optimal decision stump. Then, the decision stumps are assembled in a decision tree, Bandit Tree, and in a random collection of decision trees, Bandit Forest. We show that the proposed algorithms are optimal up to a logarit...

Classification is an important problem in data mining. Decision tree induction is one of the most common techniques that are applied to solve the classification problem. Many decision tree induction algorithms have been proposed based on different attribute selection and pruning strategies. Although the patterns induced by decision trees are easy to interpret and comprehend compare to the patte...