این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Iranian Journal of Fuzzy Systems، جلد ۳، شماره ۱، صفحات ۷۷-۸۹
عنوان فارسی NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
چکیده فارسی مقاله Designing an effective criterion for selecting the best rule is a major problem in the
process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence
and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this
paper new entities namely precision and recall from the field of Information Retrieval (IR)
systems is adapted as alternative criteria for fuzzy rule evaluation. Several different
combinations of precision and recall are redesigned to produce a metric measure. These newly
introduced criteria are utilized as a rule selection mechanism in the method of Iterative Rule
Learning (IRL) of FLC. In several experiments, three standard datasets are used to compare and
contrast the novel IR based criteria with other previously developed measures. Experimental
results illustrate the effectiveness of the proposed techniques in terms of classification
performance and computational efficiency.
کلیدواژه‌های فارسی مقاله Fuzzy classification، Rule evaluation criteria، Information retrieval، Iterative rule learning،
عنوان انگلیسی NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
چکیده انگلیسی مقاله Designing an effective criterion for selecting the best rule is a major problem in the
process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence
and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this
paper new entities namely precision and recall from the field of Information Retrieval (IR)
systems is adapted as alternative criteria for fuzzy rule evaluation. Several different
combinations of precision and recall are redesigned to produce a metric measure. These newly
introduced criteria are utilized as a rule selection mechanism in the method of Iterative Rule
Learning (IRL) of FLC. In several experiments, three standard datasets are used to compare and
contrast the novel IR based criteria with other previously developed measures. Experimental
results illustrate the effectiveness of the proposed techniques in terms of classification
performance and computational efficiency.
کلیدواژه‌های انگلیسی مقاله Fuzzy classification, Rule evaluation criteria, Information retrieval, Iterative rule learning
نویسندگان مقاله مهدی افتخاری |
department of computer science and engineering, shiraz university, shiraz, iran
سازمان اصلی تایید شده: دانشگاه شیراز (Shiraz university)

منصور ذوالقدری جهرمی | zolghadri jahromi
department of computer science and engineering, shiraz university, shiraz, iran
سازمان اصلی تایید شده: دانشگاه شیراز (Shiraz university)

سراج الدین کاتبی |
department of computer science and engineering, shiraz university, shiraz, iran
سازمان اصلی تایید شده: دانشگاه شیراز (Shiraz university)

نشانی اینترنتی http://ijfs.usb.ac.ir/article_439_f7e8f096de34f37fde594f63919275d0.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده fa
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات