40 research outputs found
Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data
BACKGROUND: Due to the large number of genes in a typical microarray dataset, feature selection looks set to play an important role in reducing noise and computational cost in gene expression-based tissue classification while improving accuracy at the same time. Surprisingly, this does not appear to be the case for all multiclass microarray datasets. The reason is that many feature selection techniques applied on microarray datasets are either rank-based and hence do not take into account correlations between genes, or are wrapper-based, which require high computational cost, and often yield difficult-to-reproduce results. In studies where correlations between genes are considered, attempts to establish the merit of the proposed techniques are hampered by evaluation procedures which are less than meticulous, resulting in overly optimistic estimates of accuracy. RESULTS: We present two realistically evaluated correlation-based feature selection techniques which incorporate, in addition to the two existing criteria involved in forming a predictor set (relevance and redundancy), a third criterion called the degree of differential prioritization (DDP). DDP functions as a parameter to strike the balance between relevance and redundancy, providing our techniques with the novel ability to differentially prioritize the optimization of relevance against redundancy (and vice versa). This ability proves useful in producing optimal classification accuracy while using reasonably small predictor set sizes for nine well-known multiclass microarray datasets. CONCLUSION: For multiclass microarray datasets, especially the GCM and NCI60 datasets, DDP enables our filter-based techniques to produce accuracies better than those reported in previous studies which employed similarly realistic evaluation procedures
Rectified softmax loss with all-sided cost sensitivity for age estimation
In Convolutional Neural Network (ConvNet) based age estimation algorithms, softmax loss is usually chosen as the loss function directly, and the problems of Cost Sensitivity (CS), such as class imbalance and misclassification cost difference between different classes, are not considered. Focus on these problems, this paper constructs a rectified softmax loss function with all-sided CS, and proposes a novel cost-sensitive ConvNet based age estimation algorithm. Firstly, a loss function is established for each age category to solve the imbalance of the number of training samples. Then, a cost matrix is defined to reflect the cost difference caused by misclassification between different classes, thus constructing a new cost-sensitive error function. Finally, the above methods are merged to construct a rectified softmax loss function for ConvNet model, and a corresponding Back Propagation (BP) training scheme is designed to enable ConvNet network to learn robust face representation for age estimation during the training phase. Simultaneously, the rectified softmax loss is theoretically proved that it satisfies the general conditions of the loss function used for classification. The effectiveness of the proposed method is verified by experiments on face image datasets of different races. © 2013 IEEE
Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data
Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models
Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques
Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
Domain adaptation aims to transfer knowledge from a domain with adequate
labeled samples to a domain with scarce labeled samples. Prior research has
introduced various open set domain adaptation settings in the literature to
extend the applications of domain adaptation methods in real-world scenarios.
This paper focuses on the type of open set domain adaptation setting where the
target domain has both private ('unknown classes') label space and the shared
('known classes') label space. However, the source domain only has the 'known
classes' label space. Prevalent distribution-matching domain adaptation methods
are inadequate in such a setting that demands adaptation from a smaller source
domain to a larger and diverse target domain with more classes. For addressing
this specific open set domain adaptation setting, prior research introduces a
domain adversarial model that uses a fixed threshold for distinguishing known
from unknown target samples and lacks at handling negative transfers. We extend
their adversarial model and propose a novel adversarial domain adaptation model
with multiple auxiliary classifiers. The proposed multi-classifier structure
introduces a weighting module that evaluates distinctive domain characteristics
for assigning the target samples with weights which are more representative to
whether they are likely to belong to the known and unknown classes to encourage
positive transfers during adversarial training and simultaneously reduces the
domain gap between the shared classes of the source and target domains. A
thorough experimental investigation shows that our proposed method outperforms
existing domain adaptation methods on a number of domain adaptation datasets.Comment: Accepted in IEEE Transactions on Multimedia (in press), 202
Distortion Robust Image Classification using Deep Convolutional Neural Network with Discrete Cosine Transform
Convolutional Neural Network is good at image classification. However, it is
found to be vulnerable to image quality degradation. Even a small amount of
distortion such as noise or blur can severely hamper the performance of these
CNN architectures. Most of the work in the literature strives to mitigate this
problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a
union set of distorted training data. This iterative fine-tuning process with
all known types of distortion is exhaustive and the network struggles to handle
unseen distortions. In this work, we propose distortion robust DCT-Net, a
Discrete Cosine Transform based module integrated into a deep network which is
built on top of VGG16. Unlike other works in the literature, DCT-Net is "blind"
to the distortion type and level in an image both during training and testing.
As a part of the training process, the proposed DCT module discards input
information which mostly represents the contribution of high frequencies. The
DCT-Net is trained "blindly" only once and applied in generic situation without
further retraining. We also extend the idea of traditional dropout and present
a training adaptive version of the same. We evaluate our proposed method
against Gaussian blur, motion blur, salt and pepper noise, Gaussian noise and
speckle noise added to CIFAR-10/100 and ImageNet test sets. Experimental
results demonstrate that once trained, DCT-Net not only generalizes well to a
variety of unseen image distortions but also outperforms other methods in the
literature
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Network representation learning (NRL) is an effective graph analytics
technique and promotes users to deeply understand the hidden characteristics of
graph data. It has been successfully applied in many real-world tasks related
to network science, such as social network data processing, biological
information processing, and recommender systems. Deep Learning is a powerful
tool to learn data features. However, it is non-trivial to generalize deep
learning to graph-structured data since it is different from the regular data
such as pictures having spatial information and sounds having temporal
information. Recently, researchers proposed many deep learning-based methods in
the area of NRL. In this survey, we investigate classical NRL from traditional
feature learning method to the deep learning-based model, analyze relationships
between them, and summarize the latest progress. Finally, we discuss open
issues considering NRL and point out the future directions in this field