An Accurate Fuzzy Frequent Pattern Based Classifier Using Confidence Tuning
محل انتشار: نخستین کنفرانس ملی محاسبات نرم
سال انتشار: 1394
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 408
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شناسه ملی سند علمی:
CSCG01_189
تاریخ نمایه سازی: 29 مهر 1396
چکیده مقاله:
Associative classifier algorithms combine two data mining paradigms, namely sample classification andassociation rule mining. These methods are very interesting for building an accurate classification model in a wide area of real-world applications. Lately, many methods have been presented to integrate associative classifiers with fuzzy set theory, in order to improve the quality of previous algorithms. This paper presents a three-step fuzzy frequent pattern (FFP) based classifier which uses an Apriori like algorithm to generate a large number of FFPs from each data class. Our algorithm in the second stepselects a subset of useful FFPs and removes redundant ones. Finally, in order to tune the boundaries between various data classes, we use a confidence improvement process. We tested our algorithm on six real-world datasets and compared the achieved results with two well-known fuzzy associative classifier algorithms.
کلیدواژه ها:
نویسندگان
Alireza Hekmatinia
Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran
Mohammad Saniee Abadeh
Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran