Data-driven constructive induction

Bloedorn, Eric; Michalsi, R.S.

doi:10.1109/5254.671089

Cited by 56 publications

(27 citation statements)

References 5 publications

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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%

“…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%

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Zheng

2000

Machine Learning

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Abstract. While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, X -of-N . An X-of-N representation is a set containing one or more attribute-value pairs. For a given instance, the value of an X -of-N representation corresponds to the number of its attribute-value pairs that are true of the instance. A single X -of-N representation can directly and simply represent any concept that can be represented by a single conjunctive, a single disjunctive, or a single M-of-N representation commonly used for constructive induction, and the reverse is not true. In this paper, we describe a constructive decision tree learning algorithm, called XofN. When building decision trees, this algorithm creates one X -of-N representation, either as a nominal attribute or as a numeric attribute, at each decision node. The construction of X -of-N representations is carried out by greedily searching the space defined by all the attribute-value pairs of a domain. Experimental results reveal that constructing X -of-N attributes can significantly improve the performance of decision tree learning in both artificial and natural domains in terms of higher prediction accuracy and lower theory complexity. The results also show the performance advantages of constructing X -of-N attributes over constructing conjunctive, disjunctive, or M-of-N representations for decision tree learning.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Zheng

2000

Machine Learning

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“…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%

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2000

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Abstract. This paper describes a method for selecting training examples for a partial memory learning system. The method selects extreme examples that lie at the boundaries of concept descriptions and uses these examples with new training examples to induce new concept descriptions. Forgetting mechanisms also may be active to remove examples from partial memory that are irrelevant or outdated for the learning task. Using an implementation of the method, we conducted a lesion study and a direct comparison to examine the effects of partial memory learning on predictive accuracy and on the number of training examples maintained during learning. These experiments involved the STAGGER Concepts, a synthetic problem, and two real-world problems: a blasting cap detection problem and a computer intrusion detection problem. Experimental results suggest that the partial memory learner notably reduced memory requirements at the slight expense of predictive accuracy, and tracked concept drift as well as other learners designed for this task.

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“…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).…”

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confidence: 99%

Improving prediction models applied in systems monitoring natural hazards and machinery

Sikora

2012

International Journal of Applied Mathematics and Computer Science

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A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.

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Data-driven constructive induction

Cited by 56 publications

References 5 publications

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Improving prediction models applied in systems monitoring natural hazards and machinery

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