ارائه الگوریتم ترکیبی پالایشی-پوششی انتخاب ویژگی‌ و کاربرد آن در کاهش بعد داده های بیان ژن

نویسندگان

دانشگاه قم

چکیده

امروزه بالا رفتن حجم داده‌ها و تعداد ویژگی‌ها‌ در مجموعه داده،­ باعث کاهش دقت الگوریتم یادگیری و پیچیدگی محاسباتی شده است. . روش­های کاهش بعد، نوعی از روش انتخاب مشخصه‌ هستند که به دو صورت پالایشی و پوششی انجام می­شود. دقت روش‌های پوششی نسبت به روش‌های پالایشی بالاتر است اما در مقابل، روش‌های پالایشی­ سریع­تر عمل می­کنند و پیچیدگی­های محاسباتی کمتری دارند. با در نظر گرفتن مزایا و معایب الگوریتم­های پالایشی و پوششی، در این پژوهش یک روش ترکیبی جدید ارائه شده است. در این روش، ابتدا کل مشخصه‌های موجود در مجموعه داده­ در نظر گرفته می­شوند سپس با ترکیب الگوریتم­های پالایشی انتخاب مشخصه‌ و ارزش­گذاری نتایج آن­ به روش پوششی، زیرمجموعه­ای بهینه از مشخصه‌ها انتخاب می­شوند. با توجه به اینکه بسیاری از بیماری­ها و مسائل زیست سیستمی نظیر سرطان، به کمک بررسی داده­ی­ ریزآرایه قابل شناسایی و تشخیص هستند و با توجه به اینکه تعداد مشخصه­ها در این مجموعه داده­ها بسیار بالا است؛ روش­ ارائه شده در این پژوهش برروی داده­ی­ ریزآرایه مربوط به سه نوع سرطان مورد ارزیابی قرار گرفته­ است.این روش، در مقایسه با روش‌های مشابه، به دقت بالایی در دسته‌بندی و شناسایی عوامل مؤثر در سرطان به خصوص سرطان خون دست یافته است.

کلیدواژه‌ها

عنوان مقاله [English]

Developing a Filter-Wrapper Feature Selection Method and its Application in Dimension Reduction of Gen Expression

نویسندگان [English]

  • zahra roozbahani
  • m yari
  • razieh ghiasi
چکیده [English]

Nowadays, increasing the volume of data and the number of attributes in the dataset has reduced the accuracy of the learning algorithm and the computational complexity. A dimensionality reduction method is a feature selection method, which is done through filtering and wrapping. The wrapper methods are more accurate than filter ones but perform faster and have a less computational burden. With respect to the advantages and disadvantages of the filter and wrapper algorithms, a new hybrid approach is proposed in this study. In the method, all features in the dataset are considered, then the optimal subset of features is selected by combining the feature selection filter algorithms and evaluating their results using the wrapper method. Considering the many diseases and biosystem issues, such as cancer, can be identified and diagnosed by microarray data analysis and considering that there are many features in such datasets, the method proposed in this paper has been evaluated on microarray data related to three types of cancers.  Compared with similar methods, the results show the proposed method benefits from high accuracy in classifying and identifying the affecting factors on cancer.​

کلیدواژه‌ها [English]

  • dimension reduction
  • feature selection
  • filter-wrapper
  • multi layer perception neural network
  • micro-array

مراجع

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سابقه مقاله

  • تاریخ دریافت: 02 خرداد 1400
  • تاریخ بازنگری: 01 دی 1403
  • تاریخ پذیرش: 02 خرداد 1400

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