Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Implementasi Pengklasifikasi Segmen Vaskular Retina Mata Dengan Metode M-Medios Multivariat

Neovaskularisasi adalah berkembangnya pembuluh darah baru di dalam mata. Pembuluh darah ini merupakan pembuluh darah yang abnormal, memiliki dinding pembuluh yang tipis, lemah, dan mudah pecah. Neovaskularisasi dapat terbentuk pada berbagai lokasi di dalam mata termasuk pada retina, sehingga citra retina dapat digunakan untuk mendeteksi neovaskularisasi secara otomatis. Pendeteksian dilakukan dengan melakukan klasifikasi terhadap segmen vaskular retina mata sebagai segmen vaskular normal atau abnormal. Tugas akhir ini mengimplementasikan salah satu metode pengklasifikasi yang dapat digunakan untuk klasifikasi segmen vaskular retina yaitu m-Mediods multivariat. Metode m-Mediods multivariat terdiri dari dua tahap. Tahap pertama adalah perbaikan ruang fitur menggunakan Local Fisher Discriminant Analysis. Tahap kedua merupakan tahap klasifikasi dengan metode Learning Vector Quantization. Sebelum dilakukan klasifikasi, terlebih dahulu dilakukan praproses dengan metode masking untuk memisahkan antara latar belakang gambar dan objek retina. Selanjutnya dilakukan segmentasi pembuluh darah menggunakan transformasi wavelet yaitu dengan Isotropic Undecimated Wavelet Transform. Tahap awal sebelum klasifikasi adalah ekstraksi ciri untuk menghasilkan vektor fitur yang digunakan sebagai pembeda segmen vaskular normal dan abnormal. Sebelum akhirnya dilakukan klasifikasi segmen vaskular dengan metode m-Mediods multivariat. Hasil uji coba dari hasil segmentasi vaskular dari citra retina pada basis data DRIVE menghasilkan nilai akurasi mencapai 95,04% dengan perbandingan citra ground truth. Sedangkan hasil klasifikasi segmen vaskular normal dan abnormal dengan citra retina dari basis data STARE menggunakan metode m-Mediods multivariat menunjukkan akurasi terbaik sebesar 96.2%. Sehingga dapat disimpulkan bahwa metode segmentasi vaskular retina dan klasifikasi segmen vaskular retina yang digunakan pada Tugas akhir ini mampu melakukan segmentasi dan klasifikasi dengan baik. ============================================================================================================================= Neovascularization is development of new blood vessels in the eyes. These new blood vessels are abnormal blood vessels, have the vessel walls are thin, weak, and easily broken. Neovascularization can be formed at various locations within the eyes including the retina, hence the retinal image can be used to detect neovascularization. Detection can be done by classifying retinal vascular segments as a normal or an abnormal vascular segment. The final project is to implement one of the methods classifiers that can be used for classification of retinal vascular segments namely m-Mediods multivariate. M-Mediods multivariate method consists of two stages. The first stage is the improvement feature space using local fisher discriminant analysis. The second stage is the stage of classification with learning vector quantization method. Before the classification, preprocessing is done by masking method to separate the object of the retina from the background image. Vascular segmentation is then performed using the wavelet transform namely undecimated isotropic wavelet transform. The initial phase of classification is feature extraction to produce a feature vector which is used to distinguish normal and abnormal vascular segment. Finally classification of vascular segments is done using m-Mediods multivariate methods. The results of the evaluation of vascular segmentation with retinal image from DRIVE database generate the accuracy value of 95.04% with ground truth image comparison. While the results of the classification of normal and abnormal vascular segments by retinal image from STARE database using multivariate m- Mediods methods shows the best accuracy of 96.2%. Therefor it can be concluded that the method used for retinal vascular segmentation and retinal vascular segments classification in this final project is reliable for segmentation and classification