نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری گروه مدیریت صنعتی دانشگاه آزاد اسلامی واحد علوم و تحقیقات
2 دانشیار دانشکده مدیریت و حسابداری دانشگاه شهید بهشتی
3 استادیار دانشکده مدیریت و اقتصاد دانشگاه آزاد اسلامی واحد علوم و تحقیقات
چکیده
مدل های مبتنی بر روابط برتری یک شاخه مهم از روشهای تصمیم چندمعیاره هستند که نیاز به تعریف مقدار قابل
توجهی اطلاعات ترجیحی در قالب پارامترها توسط تصمیم گیرنده دارند. تعدد پارامترها، معنای اغلب گیی کننیده
آنها در فضای مسئله و طبیعت غیردقیق دادهها، این فرآیند را خصوصاً در مسائل طبقه بندی اعتباری با ابعیاد بیزر
که نیاز به تصمیمگیری بلادرنگ است بسیار زمانبیر و پییییده میی سیازد. بیدین منریور روی یرد ت مییی زداییی
ترجیحات این اطلاعات را از طریق قضاوتهای جامعی که توسط تصمیم گیرنده فراهم می شوند استنتاج می کند.
این روی رد در تصمیم گیری چندمعیاره معادل یادگیری ماشینی در حوزه هوش مصنوعی است.
تحت این روی رد، ایین مقالیه ییک روش جدیید پیشینهاد میی کنید کیه در آن الریوریتم ینتییک طیی فرآینید
یادگیری، به طور همزمان تمامی پارامترهای میدل ELECTRE TRI را از داده هیای آموزشیی اسیتنتاج و در
خاتمه فرآیند، پارامترهای استنتاج شده بیرای طبقیه بنیدی داده هیای آزمایشیی ب یار گرفتیه میی شیوند. تحلییل
آزمایشات روی دیتاست های اعتباری نشان از کیفیت بالا و قابل رقابت روش پیشنهادی در مقایسه با مدل هیا ی
استاندارد طبقهبندی دارد.
کلیدواژه‌ها
عنوان مقاله [English]
An evolutionary method for credit scoring; Preference Disaggregation approach
نویسندگان [English]
- Amir Daneshvar 1
- Mostafa Zandieh 2
- Jamshid Nazemi 3
1
2
3
چکیده [English]
Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.
Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.
Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.
کلیدواژه‌ها [English]
- Credit Scoring
- ELECTRE TRI
- Preference Disaggregation
- Machine Learning
- Genetic Algorithm(GA)
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