Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros, André C. P. L. F. de Carvalho, Alex A.

By Rodrigo C. Barros, André C. P. L. F. de Carvalho, Alex A. Freitas

Provides a close learn of the key layout parts that represent a top-down decision-tree induction set of rules, together with elements similar to cut up standards, preventing standards, pruning and the methods for facing lacking values. while the method nonetheless hired these days is to exploit a 'generic' decision-tree induction set of rules whatever the facts, the authors argue at the advantages bias-fitting method may perhaps carry to decision-tree induction, during which the last word aim is the automated new release of a decision-tree induction set of rules adapted to the appliance area of curiosity. For such, they speak about how you can successfully detect the main appropriate set of parts of decision-tree induction algorithms to house a wide selection of purposes throughout the paradigm of evolutionary computation, following the emergence of a singular box known as hyper-heuristics.

"Automatic layout of Decision-Tree Induction Algorithms" will be hugely worthy for computing device studying and evolutionary computation scholars and researchers alike.

Show description

Read Online or Download Automatic Design of Decision-Tree Induction Algorithms (Springer Briefs in Computer Science) PDF

Best algorithms books

Fundamentals of Algorithmics

Notice: quality B/W experiment with colour entrance & again covers.

this is often an introductory-level set of rules e-book. It comprises worked-out examples and precise proofs. provides Algorithms by way of kind relatively than program. comprises established fabric by means of thoughts hired, no longer by means of the appliance quarter, so readers can growth from the underlying summary thoughts to the concrete software necessities. It starts off with a compact, yet entire advent to a couple worthwhile math. And it methods the research and layout of algorithms via sort instead of through software.

Algorithms and Programming: Problems and Solutions (2nd Edition) (Springer Undergraduate Texts in Mathematics and Technology)

"Algorithms and Programming" is essentially meant for a primary yr undergraduate path in programming. established in a problem-solution structure, the textual content motivates the scholar to imagine during the programming technique, therefore constructing a company realizing of the underlying idea. even though a reasonable familiarity with programming is believed, the publication is well used by scholars new to computing device technological know-how.

Nonlinear Assignment Problems: Algorithms and Applications

Nonlinear task difficulties (NAPs) are normal extensions of the vintage Linear task challenge, and regardless of the efforts of many researchers during the last 3 many years, they nonetheless stay many of the toughest combinatorial optimization difficulties to resolve precisely. the aim of this booklet is to supply in one quantity, significant algorithmic points and functions of NAPs as contributed by way of top foreign specialists.

OpenCL in Action: How to Accelerate Graphics and Computations

Precis OpenCL in motion is a radical, hands-on presentation of OpenCL, with an eye fixed towards exhibiting builders tips to construct high-performance functions in their personal. It starts off by way of featuring the middle ideas in the back of OpenCL, together with vector computing, parallel programming, and multi-threaded operations, after which publications you step by step from easy info buildings to advanced capabilities.

Additional info for Automatic Design of Decision-Tree Induction Algorithms (Springer Briefs in Computer Science)

Example text

Kass, An exploratory technique for investigating large quantities of categorical data. APPL STATIST 29(2), 119–127 (1980) 56. B. Kim, D. Landgrebe, Hierarchical classifier design in high-dimensional numerous class cases. IEEE Trans. Geosci. Remote Sens. 29(4), 518–528 (1991) References 43 57. I. Kononenko, I. Bratko, E. Roskar, Experiments in automatic learning of medical diagnostic rules. Technical Report Ljubljana, Yugoslavia: Jozef Stefan Institute (1984) 58. I. Kononenko, Estimating attributes: analysis and extensions of RELIEF, Proceedings of the European Conference on Machine Learning on Machine Learning (Springer, New York, 1994).

W. Hsiao, Y. Shih, Splitting variable selection for multivariate regression trees. Stat. Probab. Lett. 77(3), 265–271 (2007) 49. B. Hunt, J. J. Stone, Experiments in Induction (Academic Press, New York, 1966) 50. L. Hyafil, R. Rivest, Constructing optimal binary decision trees is NP-complete. Inf. Process. Lett. 5(1), 15–17 (1976) 51. A. Ittner, Non-linear decision trees, in 13th International Conference on Machine Learning. pp. 1–6 (1996) 52. B. , A new criterion in selection and discretization of attributes for the generation of decision trees.

2 It automatically adjusts for variations in the number of instances from node to node because its distribution does not change with the number of instances at each node. Quinlan and Rivest [96] propose using the minimum description length principle (MDL) as a splitting measure for decision-tree induction. MDL states that, given a set of competing hypotheses (in this case, decision trees), one should choose as the preferred hypothesis the one that minimizes the sum of two terms: (i) the description length of the hypothesis (dl ); and (ii) length of the data given the hypothesis (lh ).

Download PDF sample

Rated 4.46 of 5 – based on 42 votes