Download Demand-Driven Associative Classification by Adriano Veloso PDF

By Adriano Veloso

The final objective of machines is to aid people to resolve problems.
Such difficulties variety among extremes: dependent difficulties for which the answer is completely outlined (and hence are simply programmed through humans), and random difficulties for which the answer is totally undefined (and hence can't be programmed). difficulties within the immense center floor have ideas that can't be good outlined and are, therefore, inherently demanding to software. laptop studying is the best way to deal with this immense center flooring, in order that many tedious and hard hand-coding initiatives will be changed by way of automated studying equipment. There are a number of laptop studying initiatives, and this paintings is concentrated on a huge one, that's referred to as category. a few type difficulties are difficult to unravel, yet we exhibit that they are often decomposed into a lot less complicated sub-problems. We additionally exhibit that independently fixing those sub-problems by means of considering their specific calls for, frequently ends up in stronger class performance.

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Extra resources for Demand-Driven Associative Classification

Example text

All extracted rules are useful to xi : Basic steps for LAC-MR are shown in Algorithm 5. 1. There are ten pairs in S: There is one input in T; x12 ; for which the corresponding output, y12 ; is unknown. All inputs were discretized. 2. 30. In this case, Rx12 contains the following 6 rules: 1. 2. 3. 4. fa1 fa1 fa2 fa2 ¼ ½0:46À1:00Š ! output ¼ 1gðh ¼ 1:00Þ ¼ ½0:46À1:00Š ^ a3 ¼ ½0:35À1:00Š ! output ¼ 1gðh ¼ 1:00Þ ¼ ½0:00À0:33Š ! output ¼ 0gðh ¼ 0:67Þ ¼ ½0:00À0:33Š ^ a3 ¼ ½0:35À1:00Š ! 00] 0 1 0 1 0 5.

Output ¼ 1gðh ¼ 1:00Þ ¼ ½0:46À1:00Š ^ a3 ¼ ½0:35À1:00Š ! output ¼ 1gðh ¼ 1:00Þ ¼ ½0:00À0:33Š ! output ¼ 0gðh ¼ 0:67Þ ¼ ½0:00À0:33Š ^ a3 ¼ ½0:35À1:00Š ! 00] 0 1 0 1 0 5. fa3 ¼ ½0:35À1:00Š ! output ¼ 0gðh ¼ 0:60Þ 6. fa3 ¼ ½0:35À1:00Š ! output ¼ 1gðh ¼ 0:40Þ According to Eq. LAC-MR predicts output 1 for input x12 ; which is the correct one. 2 Demand-Driven Function Approximation A complex target function may be composed of simple parts. , fSxi ). Intuitively, such simple functions are more likely to generalize than a single complex function.

275–282. (2002) 12. : Efficient classification based on multiple class-association rules. In: Proceedings of the International Conference on Data Mining (ICDM), pp. 369–376. IEEE Computer Society (2001) 13. : Integrating classification and association rule mining. In: Proceedings of the Conference on Data Mining and Knowledge Discovery (KDD), pp. 80–86. ACM Press (1998) 14. : Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization.

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