Temporal Decision Trees or the lazy ECU vindicated

نویسندگان

  • Luca Console
  • Claudia Picardi
  • Daniele Theseider Dupré
چکیده

The automatic generation of diagnostic decision trees from qualitative models is a reasonable compromise between the advantages of using a modelbased approach in technical domains and the constraints imposed by on-board applications. In this paper we extend the approach to deal with temporal information. We introduce a notion of temporal diagnostic decision tree, in which nodes have a temporal label providing temporal constraints on the observations, and we present an algorithm for compiling such trees from a model-based diagnostic system.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Batched Lazy Decision Trees

We introduce a batched lazy algorithm for supervised classification using decision trees. It avoids unnecessary visits to irrelevant nodes when it is used to make predictions with either eagerly or lazily trained decision trees. A set of experiments demonstrate that the proposed algorithm can outperform both the conventional and lazy decision tree algorithms in terms of computation time as well...

متن کامل

Boosting Lazy Decision Trees

This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy decision tree ensemble customized for each test instance. From the experimental results, we conclude that our boosting-style algorithm significantly improves the performance of the base learner. An empirical comparison...

متن کامل

Combining two lazy learning methods for classification and knowledge discovery

The goal of this paper is to construct a classifier for diagnosing malignant melanoma. We experimented with two lazy learning methods, $k$-NN and \textsf{LID}, and compared their results with the ones produced by decision trees. We performed this comparison because we are also interested on building a domain model that can serve as basis to dermatologists to propose a good characterization of e...

متن کامل

Combining two lazy learning methods for classification and knowledge discovery

The goal of this paper is to construct a classifier for diagnosing malignant melanoma. We experimented with two lazy learning methods, $k$-NN and \textsf{LID}, and compared their results with the ones produced by decision trees. We performed this comparison because we are also interested on building a domain model that can serve as basis to dermatologists to propose a good characterization of e...

متن کامل

Lazy Decision Trees

Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single “best” decision tree during the training phase, and this tree is then used to classify test instances. The tests ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2001