Comparison of Classifier Selection Methods for Improving Committee Performance

نویسنده

  • Matti Aksela
چکیده

Combining classifiers is an effective way of improving classification performance. In many situations it is possible to construct several classifiers with different characteristics. Selecting the member classifiers with the best individual performance can be shown to be suboptimal in several cases, and hence there exists a need to attempt to find effective member classifier selection methods. In this paper six selection criteria are discussed and evaluated in the setting of combining classifiers for isolated handwritten character recognition. A criterion focused on penalizing many classifiers making the same error, the exponential error count, is found to be able to produce the best selections.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering

Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...

متن کامل

The Comparison of Four Economical Selection Indices for Improving the Performance of Kermani Sheep under Rural Production System

Developing effective selection programs for improving the performance of livestock requires taking the economical selection indices into account. The present study was performed for comparing four selection indices developed in Kermani sheep through simulated data. The relative importance of traits was determined based on the estimated economic values and consequently, the most beneficial trait...

متن کامل

A New Hybrid Framework for Filter based Feature Selection using Information Gain and Symmetric Uncertainty (TECHNICAL NOTE)

Feature selection is a pre-processing technique used for eliminating the irrelevant and redundant features which results in enhancing the performance of the classifiers. When a dataset contains more irrelevant and redundant features, it fails to increase the accuracy and also reduces the performance of the classifiers. To avoid them, this paper presents a new hybrid feature selection method usi...

متن کامل

Effective Feature Selection for Pre-Cancerous Cervix Lesions Using Artificial Neural Networks

Since most common form of cervical cancer starts with pre-cancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. There are 2 ways to stop this disease from developing. One way is to find and treat pre-cancers before they become true cancers, and the other is to prevent the pre-cancers in the first place. The presented approach...

متن کامل

ارتقای کیفیت دسته‌بندی متون با استفاده از کمیته‌ دسته‌بند دو سطحی

Nowadays, the automated text classification has witnessed special importance due to the increasing availability of documents in digital form and ensuing need to organize them. Although this problem is in the Information Retrieval (IR) field, the dominant approach is based on machine learning techniques. Approaches based on classifier committees have shown a better performance than the others. I...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003