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Active Learning Technique

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Lorenzo Bruzzone – One of the best experts on this subject based on the ideXlab platform.

  • A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
    Co-Authors: Swarnajyoti Patra, Kaushal Bhardwaj, Lorenzo Bruzzone

    Abstract:

    Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a  novel Technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with Active Learning. The proposed Technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image, then extended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an Active Learning Technique. This Technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of $k$ -means clustering, $K$ -nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art Active Learning methods.

  • A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification
    IEEE Transactions on Geoscience and Remote Sensing, 2014
    Co-Authors: Swarnajyoti Patra, Lorenzo Bruzzone

    Abstract:

    In this paper, a novel iterative Active Learning Technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The Technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.

  • a genetic algorithm based cost sensitive Active Learning Technique for classification of remote sensing images
    2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), 2012
    Co-Authors: Begum Demir, Luca Minello, Lorenzo Bruzzone

    Abstract:

    This paper proposes a novel cost-sensitive Active Learning (CSAL) Technique for the classification of remote sensing images with Support Vector Machines. The proposed Technique assumes that the labeling cost of samples during ground survey depends on both the samples accessibility and the traveling time to the considered locations. Thus, it is not equal for the samples on the ground. Accordingly, the proposed method aims at selecting the most informative (the most uncertain and diverse) as well as cost-efficient samples at each iteration of the Active Learning process. This is accomplished according to three steps. In the first step the most uncertain unlabeled samples are selected by using the multiclass-level uncertainty Technique. In the second step, the small (and important) portion of the image, in which the highest density of the most informative samples exists, is selected to effectively limit the study area. The objective of restricting the study area to a small portion of the image is to reduce the traveling time for labeling the samples. This is achieved on the basis of a novel clustering based approach. In the third step, uncertain samples that are diverse and cost-efficient are selected from the small portion of the image chosen at the second step. The selection of cost-efficient diverse samples is achieved on the basis of a genetic algorithm. Thanks to the second and third steps of the proposed CSAL Technique, the cost of sample labeling is significantly reduced, while obtaining accurate classification maps. Experimental results show the success of the proposed CSAL method compared to the most promising literature Active Learning methods.

Swarnajyoti Patra – One of the best experts on this subject based on the ideXlab platform.

  • A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
    Co-Authors: Swarnajyoti Patra, Kaushal Bhardwaj, Lorenzo Bruzzone

    Abstract:

    Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a  novel Technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with Active Learning. The proposed Technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image, then extended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an Active Learning Technique. This Technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of $k$ -means clustering, $K$ -nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art Active Learning methods.

  • A fast partition-based batch-mode Active Learning Technique using SVM classifier
    Soft Computing, 2017
    Co-Authors: Anshu Singla, Swarnajyoti Patra

    Abstract:

    The selection of informative samples known as query selection is the most challenging task in Active Learning. In this article, a batch-mode Active Learning Technique is presented by defining a novel query function. The proposed Technique first divides the unlabeled samples into uniform partitions in one-dimensional feature space according to their distribution in the original feature space. Then to select the most informative samples from the unlabeled pool, one sample from each partition is selected based on an uncertainty criterion defined by exploiting SVM classifier. The number of unlabeled samples selected at each iteration of Active Learning is determined automatically and depends on the number of non-empty partitions generated. The effectiveness of the proposed Technique is measured by comparing it with four state-of-the-art Techniques exist in the literature by using four different UCI repository data sets. The experimental analysis proved that the proposed Technique is robust and computationally less demanding.

  • A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification
    IEEE Transactions on Geoscience and Remote Sensing, 2014
    Co-Authors: Swarnajyoti Patra, Lorenzo Bruzzone

    Abstract:

    In this paper, a novel iterative Active Learning Technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The Technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.

Isna Rahmawati – One of the best experts on this subject based on the ideXlab platform.

  • TEACHING ENGLISH USING Active Learning Technique TO THE THIRD YEAR STUDENTS IN MI MUHAMMADIYAH PUCANG TULUNG KLATEN IN 2009/2010 ACADEMIC YEAR
    , 2010
    Co-Authors: Isna Rahmawati

    Abstract:

    The objective of the study is to describe the process of teaching English using Active Learning Technique, to find the strength and weakness of this Technique. The result of the study is intended to be a little contribution to the teaching English. The writer formulates the problem in from of this research as follows: “how the process of teaching English using Active Learning Technique and what is is the strength and weakness of using Active Learning Technique”. The research was done in MI Muhammadiyah Pucang, Tulung, Klaten. As the sample, the writer took 14 students of the third year. Based on the data analysis the strengths of teaching English using Active Learning Technique are: (1) the students are very happy and fun; (2) they are not easy to be bored; (3) the students have the motivation to join the class and they are Active in the classroom; (4) the students have a high interest in teaching-Learning process; (5) it makes a good cooperation or relation among the students; (6) it makes the teacher creative. The weaknesses of teaching English using Active Learning Technique are: (1) the writer must give the instruction clearly; (2) the writer needs more work preparation of Active Learning than the others Technique; (3) it spends a lot of time because the writer must repeat and give the clear instructions to the students then she must guide them. The conclusion is teaching English using Active Learning Technique can develop the student’s knowledge in language skills including listening, speaking, reading, and writing. The students can be easy to understand, memorize, and pronounce the material given by the teacher.

  • teaching english using Active Learning Technique to the third year students in mi muhammadiyah pucang tulung klaten in 2009 2010 academic year
    , 2010
    Co-Authors: Isna Rahmawati

    Abstract:

    The objective of the study is to describe the process of teaching English using Active Learning Technique, to find the strength and weakness of this Technique. The result of the study is intended to be a little contribution to the teaching English. The writer formulates the problem in from of this research as follows: “how the process of teaching English using Active Learning Technique and what is is the strength and weakness of using Active Learning Technique”. The research was done in MI Muhammadiyah Pucang, Tulung, Klaten. As the sample, the writer took 14 students of the third year. Based on the data analysis the strengths of teaching English using Active Learning Technique are: (1) the students are very happy and fun; (2) they are not easy to be bored; (3) the students have the motivation to join the class and they are Active in the classroom; (4) the students have a high interest in teaching-Learning process; (5) it makes a good cooperation or relation among the students; (6) it makes the teacher creative. The weaknesses of teaching English using Active Learning Technique are: (1) the writer must give the instruction clearly; (2) the writer needs more work preparation of Active Learning than the others Technique; (3) it spends a lot of time because the writer must repeat and give the clear instructions to the students then she must guide them. The conclusion is teaching English using Active Learning Technique can develop the student’s knowledge in language skills including listening, speaking, reading, and writing. The students can be easy to understand, memorize, and pronounce the material given by the teacher.