1998
DOI: 10.1109/5254.671089
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Data-driven constructive induction

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Cited by 56 publications
(27 citation statements)
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“…The rule learning algorithms INDUCE (Michalski, 1978), AQ17-DCI (Bloedorn & Michalski, 1998), and AQ17-MCI (Bloedorn et al, 1993) use the counting operator 21 #VarEQ(x) to construct new attributes that count the number of attributes which take the value x. For primitive boolean attributes, a boolean counting operator takes a vector of n boolean attributes (n ≥ 2) and counts the number of true values for an instance.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…The rule learning algorithms INDUCE (Michalski, 1978), AQ17-DCI (Bloedorn & Michalski, 1998), and AQ17-MCI (Bloedorn et al, 1993) use the counting operator 21 #VarEQ(x) to construct new attributes that count the number of attributes which take the value x. For primitive boolean attributes, a boolean counting operator takes a vector of n boolean attributes (n ≥ 2) and counts the number of true values for an instance.…”
Section: Related Workmentioning
confidence: 99%
“…BACON by Langley et al (1987), and INDUCE by Michalski (1978)) or attribute counting attributes 2 (e.g. INDUCE by Michalski (1978), AQ17-DCI by Bloedorn and Michalski (1998), and AQ17-MCI by Bloedorn, Michalski and Wnek (1993)). In addition, systems such as LMDT (Brodley & Utgoff, 1992), SWAP1 (Indurkhya & Weiss, 1991), and CCAF (Yip & Webb, 1994) construct linear machines (Brodley & Utgoff, 1992), linear discriminant functions, or canonical discriminant functions as new attributes.…”
Section: Introductionmentioning
confidence: 99%
“…If the learner is subsequently unable to achieve acceptable performance, then it may need to apply constructive induction operators in an effort to improve the representation space for learning. To this end, one may use a program that automatically invokes constructive induction, like AQ-18 (Bloedorn & Michalski, 1998;Kaufman & Michalski, 1998).…”
Section: Future Workmentioning
confidence: 99%
“…An additional improvement of classification and prediction abilities can be obtained by the so-called constructive induction (Bloedorn and Michalski, 2002;Wnek and Michalski, 1994). The method consists in introducing to the vector of independent variables a new variable whose values depend functionally (data driven constructive induction) or logically (hypothesis driven constructive induction) on values of the existing variables (Wnek and Michalski, 1994).…”
mentioning
confidence: 99%