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A vast amount of literature on cost-sensitive learning can be found in the online proceedings of the ICML’2000 Workshop on cost-sensitive learn- ittg. The properties of a cost matrix had been studied by Elkan in [182]. Margineantu and Dietterich [206] examined various methods for incorporating cost information into the C4.5 learning algorithm, including wrapper meth- ods, class distribution-based methods, and loss-based methods. Other cost- sensitive learning methods that are algorithm-independent include AdaCost

[t83], Metacost [177], and costing [222].

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3L2 Chapter 5 Classification: Alternative Techniques

Extensive literature is also available on the subject of multiclass learning. This includes the works of Hastie and Tibshirani [191], Allwein et al. 1156], Kong and Dietterich [201], and Tax and Duin [215]. The error-correcting output coding (ECOC) method was proposed by Dietterich and Bakiri [175]. They had also investigated techniques for designing codes that are suitable for solving multiclass problems.

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M’i,n’ing, pages 435-442, Melbourne, FL, August 2003.

5.10 Exercises

1. Consider a binary classification problem with the following set of attributes and attribute values:

o Air Conditioner : {Working, Broken}

o Engine : {Good, Bad}

o Mileage : {High, Medium, Low}

o Rust : {yes, No}

Suppose a rule-based classifier produces the following rule set:

Mileage : HiSh _- Value : Low Mileage : Low ——+ Value : High Air Conditioner : Working, Engine : Good —– Value : High Air Conditioner : Working, Engine : Bad —-+ Value : Low Air Conditioner : Brokel —+ Value : Low

(a) Are the rules mutually exclustive?



316 Chapter 5 Classification: Alternative Techniques

Is the rule set exhaustive?

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