Feature Combination for Multiclass Object Classification with Clustered Dataset
نویسنده
چکیده
In multiclass object classification, a key element is to design a robust identification of relevant class specification in presence of intra-class variations. However, this is a difficult problem due to high variability of visual appearance within each class. One possible approach is to adaptively combine a set of diverse and complementary features-such as features based on color, shape-in order to discriminate each class best from all other classes [1].
منابع مشابه
The Prediction of Booking Destination On Airbnb Dataset
This report is about analysis of the Airbnb dataset and the model we built to do the prediction task on the dataset. The dataset comes from an ongoing kaggle competition supported by Airbnb. We first did some comprehensive analysis on the dataset, explored most features and collected all features we thought was useful. Then we described and interpreted the prediction task and the evaluation met...
متن کاملAnalysis of Feature Selection Algorithms on Classification: A Survey
The aim of this paper is to discuss about various feature selection algorithms applied on different datasets to select the relevant features to classify data into binary and multi class in order to improve the accuracy of the classifier. Recent researches in medical diagnose uses the different kind of classification algorithms to diagnose the disease. For predicting the disease, the classificat...
متن کاملEfficient Feature Selection and Multiclass Classification with Integrated Instance and Model Based Learning
Multiclass classification and feature (variable) selections are commonly encountered in many biological and medical applications. However, extending binary classification approaches to multiclass problems is not trivial. Instance-based methods such as the K nearest neighbor (KNN) can naturally extend to multiclass problems and usually perform well with unbalanced data, but suffer from the curse...
متن کاملA Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis (RESEARCH NOTE)
Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...
متن کاملSVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier
Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatolo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012