Data Mining with Decision Trees - Theory and Applications

Rokach, Lior; Maimon, Oded

doi:10.1142/9789812771728

Cited by 645 publications

(675 citation statements)

References 0 publications

Supporting

Mentioning

615

Contrasting

Unclassified

Order By: Relevance

“…We used Quick, Unbiased, Efficient, Statistical trees (QUEST) [40] to determine the associations between predictor and response variables for both subpopulations. QUEST is one of several types of statistical trees; hierarchical analyses incorporating multiple predictor variables (or 'recursive partitioning') [41,42].…”

Section: Parasite Prevalence and Risk Factorsmentioning

confidence: 99%

“…in the NPA relative to PA dog subpopulations remains supported. Moreover, upon re-analysing some of the statistics using only the senior operator's data, including both the χ 2 exact test [37] used to determine off-leash frequency as a potential confounder, as well as the QUEST [40] with Giardia spp. presence or absence as the dependent variable (data not shown), the results remained unchanged.…”

Section: ·720 <0·001mentioning

confidence: 99%

See 1 more Smart Citation

Urban park-related risks forGiardiaspp. infection in dogs

et al. 2015

View full text Add to dashboard Cite

SUMMARYGiardia spp. is a common gastrointestinal (GI) parasite of multiple host species, including dogs and humans, with the potential for zoonotic transmission. The risk of GI parasitism in dogs (including Giardia spp.) may increase with park use in urban areas. This study aimed to (1) determine whether park attendance is a risk factor for Giardia spp. infection in dogs and (2) characterize the behavioural and demographic risk factors for Giardia spp. infection in park-attending and non-park-attending dogs. From August to September 2012, a total of 1293 dog owners completed a survey and 860 corresponding dog faecal samples were collected. Dog faeces were screened for Giardia spp. using a direct immunofluorescence assay and associations assessed among behaviours, demographics, and Giardia spp. infection. Main results included off-leash and swimming frequencies within parks as significantly positively associated with Giardia spp. infection in dogs. Dog-owner age was negatively associated with off-leash and swimming frequencies in parks. The results suggest some recreational behaviours in parks and certain demographics are risk factors for parasitism in pet dogs.

show abstract

Section: Parasite Prevalence and Risk Factorsmentioning

confidence: 99%

Section: ·720 <0·001mentioning

confidence: 99%

Urban park-related risks forGiardiaspp. infection in dogs

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Introducing new learning algorithms is more or less a well-defined process, since there exists an algebraic framework for presenting decision algorithms and describing various splitting criteria [66]. The data structures most frequently used for keeping decision information (trees and tables) are also well-known, along with efficient implementations once we have specified their operations.…”

Section: Motivationmentioning

confidence: 99%

“…The number of tests necessary to reach a leaf is equal to the depth of the Decision Tree. This depth varies around the number n of context attributes: for discrete context attributes it is at most n; continuous attributes can even occur several times on the path due to data partitioning [66]. So, generally, the prediction time is depth × T aa and we approximate…”

Section: Non-functional Propertiesmentioning

confidence: 99%

Decisions: Algebra and Implementation

Danylenko

Lundberg

Löwe

2011

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

Processing decision information is a constitutive part in a number of applications in Computer Science fields. In general, decision information can be used to deduce the relationship between a certain context and a certain decision. Decision information is represented by a decision model that captures this information. Frequently used examples of decision models are decision tables and decision trees. The choice of an appropriate decision model has an impact on application performance in terms of memory consumption and execution time. High memory expenses can possibly occur due to redundancy in a decision model; and high execution time is often a consequence of an unsuitable decision model. Applications in different domains try to overcome these problems by introducing new data structures or algorithms for implementing decision models. These solutions are usually domain-specific and hard to transfer from one domain to another.Different application domains of Computer Science often process decision information in a similar way and, hence, have similar problems. We should thus be able to present a unifying approach that can be applicable in all application domains for capturing and manipulating decision information. Therefore, the goal of this thesis is (i) to suggest a general structure (Decision Algebra) which provides a common theoretical framework that captures decision information and defines operations (signatures) for storing, accessing, merging, approximating, and manipulating such information along with some general algebraic laws regardless of the used implementation. Our Decision Algebra allows defining different construction strategies for decision models and data structures that capture decision information as implementation variants, and it simplifies experimental comparisons between them.Additionally, this thesis presents (ii) an implementation of Decision Algebra capturing the information in a non-redundant way and performing the operations efficiently. In fact, we show that existing decision models that originated in the field of Data Mining and Machine Learning and variants thereof as exploited in special algorithms can be understood as alternative implementation variants of the Decision Algebra by varying the implementations of the Decision Algebra operations. Hence, this work (iii) will contribute to a classification of existing technology for processing decision information in different application domains of Computer Science.

show abstract

“…Since the advent of the first decision tree (DT) learning algorithms, several decades ago, the researchers have come up with a number of criteria (called split criteria or split quality measures or selection measures) for top-down DT construction [1,10,8]. Some comparisons of such criteria [9,2] have been published.…”

Section: Introductionmentioning

confidence: 99%

Separability of Split Value Criterion with Weighted Separation Gains

Grąbczewski

2011

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

Abstract. An analysis of the Separability of Split Value criterion in some particular applications has led to conclusions about possible improvements of the criterion. Here, the new formulation of the SSV criterion is presented and examined. The results obtained for 21 different benchmark datasets are presented and discussed in comparison with the most popular decision tree node splitting criteria like information gain and Gini index. Because the new SSV definition introduces a parameter, some empirical analysis of the new parameter is presented. The new criterion turned out to be very successful in decision tree induction processes.

show abstract

Data Mining with Decision Trees - Theory and Applications

Cited by 645 publications

References 0 publications

Urban park-related risks forGiardiaspp. infection in dogs

Urban park-related risks forGiardiaspp. infection in dogs

Decisions: Algebra and Implementation

Separability of Split Value Criterion with Weighted Separation Gains

Contact Info

Product

Resources

About