A method of combining multiple probabilistic classifiers through soft competition on different feature sets

Chen, Ke; Chi, Huisheng

doi:10.1016/s0925-2312(98)00019-8

Cited by 36 publications

(16 citation statements)

References 27 publications

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“…3, it is also observed that the semi-supervised learning system of a strong classifier merely yields an improvement only when the ICA representation is used but fails to work on other two representations. By comparison, we conclude that the simultaneous use of different representations results in robust learning and better generalization during semi-supervised learning, which is completely consistent with our previous argument in unsupervised [13] and supervised learning [14], [15].…”

Section: Facial Expression Recognitionsupporting

confidence: 90%

“…One is that the nature of the task makes different redundant representations 1 available, and the other is no different representations available. For the former case, our earlier studies show the usefulness of combining multiple classifiers trained on different representations [13]- [15]. For the latter case, we can use the bootstrap re-sampling techniques [16] to create different data sets and then train an ensemble of classifiers on them.…”

Section: B Classifier Ensemble Generationmentioning

confidence: 99%

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Ensemble Learning with Active Data Selection for Semi-Supervised Pattern Classification

Wang

Chen

2007

2007 International Joint Conference on Neural Networks

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Unliketraditional pattern classification, semi-supervised learning provides a novel technique to make use of both labeled and unlabeled data for improving the performance of classification. In general, there are two critical issues for semi-supervised learning of discriminative classifiers; i.e., how to create an initial classifier of a good generalization capability with the limited labeled data and the how to make an effective use of unlabeled data without degradation of the established classifier. To tackle two aforementioned problems, we propose an ensemble learning approach based on a recent active data selection strategy [1], where ensemble learning would yield good generalization and active data selection tends to choose the unlabeled data more likely resulting in an improvement during semi-supervised learning. By using an ensemble of K-NN classifiers, we demonstrate the effectiveness of our approach on a synthetic data classification and a facial expression recognition tasks.

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Section: Facial Expression Recognitionsupporting

confidence: 90%

Section: B Classifier Ensemble Generationmentioning

confidence: 99%

Ensemble Learning with Active Data Selection for Semi-Supervised Pattern Classification

Wang

Chen

2007

2007 International Joint Conference on Neural Networks

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“…Mashao D. J. and Skosan M. [30] have combined the decision of two classifiers for improving the performance of a speaker recognition system. Doddington et al [29] suggested improvement in the base line performance by a simple combination of scores obtained for different systems. Chen and Chi [28] discussed a method of combining multiple probabilistic classifiers using different feature sets extracted for speaker identification (SI) task.…”

Section: Fusion Of Classifier Outputsmentioning

confidence: 99%

Text-independent Speaker Identification in Emotional and Whispered Speech Environments

Jawarkar¹,

Holambe²,

Basu³

2017

IJCA

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This paper describes challenging task of closed set textindependent speaker identification in emotional and whispered speech environments. In the first phase of the work, speaker identification system is developed using neutral speech and tested using speech samples comprising of six basic emotions of anger, happiness, sadness, disgust, neutral and fear. The performance is analyzed using Mel frequency cepstral coefficients (MFCC), Line spectral frequencies (LSF), and temporal energy of subband cepstral coefficients (TESBCC) feature sets. The second phase of work involves the process of speaker identification system in whispered speech environment. The performance of the speaker identification system degrades drastically for whisper speech utterances. A new feature called temporal Teager energy based subband cepstral coefficients (TTESBCC) is proposed. The comparison of the performance of MFCC, TESBCC, weighted instantaneous frequency (WIF) and TTESBCC feature sets is done for this process. A novel classifiers fusion technique is developed and its performance is compared with that of the individual classifiers. Two databases with speech utterances of thirty nine speakers recorded in the six basic emotions and speech utterances of twenty five speakers in whispered speech are used for experimentation. The speech utterances for database were recorded in Indian languageMarathi. It is observed fusion of classifiers considerably enhances the speaker identification accuracy in both emotional and whispered speech environments.

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“…Bölütlenecek görüntü serisinin bir kısmının eğitim için kullanılması [28] ise, hem kullanıcıya bağımlı hem de klinik ortamda pratik olmayan bir işlem olduğundan yaygınlaşamamıştır. Eğiticisiz yöntemlerin ise yalnızca sınırlı yarar sağladığı ve özellikle organ sınırlarında başarısız olduğu bilimsel literatürde gözlemlenmiştir [40,41]. Bu nedenlerle, eğitimi bölütleme işlemine paralel olarak gerçekleştiren uyarlamalı sınıflayıcıların kullanımı başarılı alternatif bir yöntemdir [36].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Batin Bölgesi̇ Organlarinin MR Görüntüleri̇nden Çok Aşamali Hi̇yerarşi̇k Siniflama İle Bölütlenmesi̇

Selvi¹,

Selver²,

Kavur³

et al. 2015

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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ÖZETTıbbi görüntüleme ile anatomi hakkında ayrıntılı bilgi elde edinilebildiğinden, tanı amaçlı görüntüleme günümüzde birçok açıdan önem kazanmıştır. Görüntüleme cihazları tarafından sunulan verilerin fazlalığı ve çeşitliliği nedeniyle, tüm veri yerine görüntülerde sadece ilgilenilen dokunun belirlenerek ayrılması (Bölütlenmesi) sağlanabilir. Elcil yöntemler ile bölütleme yorucu, zaman alıcı ve deneyim gerektiren bir işlem olduğundan, otomatik yordamlara gereksinim duyulmaktadır. Geliştirilen yordamların klinik koşullarında kullanılabilmesi içinse yüksek başarıma sahip sonuçlar üretmeleri gerekmektedir. Manyetik Rezonans (MR) görüntülerinden batın bölgesindeki organlarının bölütlenmesi pek çok zorluk içeren bir uygulama alanıdır ve bu konudaki çalışmalar sınırlı sayıdadır. Batın bölgesinde yer alan, karaciğer, böbrekler, dalak, pankreas, safra kesesi gibi organların MR görüntüleri kullanılarak ileri seviye tıbbi analizi ve üç boyutlu incelenmesi pek çok tıbbi prosedür için mecburi olduğundan, bu çalışmada, ilgili organların bölütlenmesinde yukarıda belirtilen zorluklara karşı gürbüz, bölütlenecek organın özellikleri ve organların birbirleriyle olan ilişkilerini (konum vb.) göz önüne alan bir sistem geliştirilmiştir. Geliştirilen sistem farklı MR sekansları ile elde edilen görüntülere uygulanarak elde edilen sonuçlar tartışılmıştır.Anahtar Kelimeler: Bölütleme, MR, hiyerarşik sınıflama, batın SEGMENTATION OF ABDOMINAL ORGANS FROM MR IMAGES USING MULTI-LEVEL HIERARCHICAL CLASSIFICATION ABSTRACTMedical imaging modalities can provide very detailed and informative mappings of the anatomy of a subject. These detailed and informative mappings can be processed to extract the information of interest instead of dealing with whole data (segmentation). Since manual segmentation on each slice is time consuming, tedious and operator dependent, automatic tools and techniques are needed. Segmentation of abdominal organs is a very challenging field of application due to overlapping intensity ranges of the organs, variations in human anatomy and pathology and the number of studies is very limited for Magnetic Resonance (MR), which is a relatively newer and rapidly developing imaging modality.Since it is obligatory to analyze and visualize MR images of abdominal organs (i.e. liver, right/left kidneys, spleen, pancreas, gall bladder) for several medical procedures, the main goal of this paper is to design and develop a segmentation system (method+software), which is robust to the challenges mentioned above, adaptive to the properties of the abdominal organs as well as to the interrelationships of these organs.

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A method of combining multiple probabilistic classifiers through soft competition on different feature sets

Cited by 36 publications

References 27 publications

Ensemble Learning with Active Data Selection for Semi-Supervised Pattern Classification

Ensemble Learning with Active Data Selection for Semi-Supervised Pattern Classification

Text-independent Speaker Identification in Emotional and Whispered Speech Environments

Batin Bölgesi̇ Organlarinin MR Görüntüleri̇nden Çok Aşamali Hi̇yerarşi̇k Siniflama İle Bölütlenmesi̇

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