Generating Knowledge-Guided Discriminative Features Using Genetic Programming for Melanoma Detection

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.

With the rapidly increasing popularity of social media sites, a large amount of user-generated data has been injected into the web. The data include a wide variety of real-world events. As a consequence, especially for social multimedia objects, it has become increasingly difficult to allow the browsing and organization of multimedia collections in a more effective manner. The approach we propose in this study addresses this problem, thus enabling the browsing and organization of multimedia collections in a natural way, i.e., by events. There have been some research studies on this problem. However, most of the previous approaches merge multiple types of features (e.g., textual content, visual content, user information and temporal information) of social media using a relatively simple mechanism. In this study, we merge multiple types of features in an integrated manner to identify the event associated with user-contributed social multimedia objects. We exploit the correlations between different types of features, i.e., textual content, visual content, user information and temporal information, to classify new social multimedia objects into their corresponding event categories. We accomplish this through a feature correlation graph (FCG) that uses features as nodes and the correlations among these features as edges for each event and individual multimedia object. We then employ a probabilistic model based on Markov random field to connect each new multimedia object with the correct event. We evaluate the algorithm on large-scale, real-world datasets of event images downloaded from Flickr, and the experimental results confirm the superiority of our approach over state-of-the-art approaches.

Genetic programming (GP) has achieved great success in evolving effective scheduling rules to make real-time decisions in dynamic flexible job shop scheduling (DFJSS). To improve generalization, a commonly used strategy is to change the training simulation(s) at each generation of the GP process. However, with such a simulation rotation, GP may lose potentially promising individuals that happen to perform poorly in one particular generation. To address this issue, this paper proposed a new multi-tree GP with archive (MTAGP) to evolve the routing and sequencing rules for DFJSS. The archive is used to store the potentially promising individuals of each generation during evolution of genetic programming. The individuals in the archive can then be fully utilized when the simulation is changed in subsequent generations. Through extensive experimental tests, the MTAGP algorithm proposed in this paper is more effective than the multi-tree GP without archive algorithm in a few scenarios. Further experiments were carried out to analyze the use of the archive and some possible guesses were ruled out. We argue that the use of archives does increase the diversity of the population. However, the number of individuals in the archive that ranked in the top five of the new population is small. Therefore, the archive may not be able to greatly improve the performance. In the future, we will investigate better ways to use the archive and better ways to update individuals in the archive.

Scene classification from images is a challenging problem in computer vision due to its significant variability of scale, illumination, and view. Recently, Latent Dirichlet Allocation (LDA) model has grown popular in computer vision field, especially in scene labeling and classification. However, the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly. Especially, there is little experimental evaluation on the model's performance for different types of features. Fusion of multiple types of features is usually necessary in the scene classification due to the complexity of scene images. In this paper, we investigate the effectiveness of the LDA model in scene classification by using 7 types of features (i.e. uniform grid based interest points, Harris corner based interest points, scale invariant feature transform (SIFT), texture, shape, color, and location) and their various combinations. Furthermore, we compare the performance of the LDA model with Support Vector Machine (SVM) classifier. All experiments are performed on the UIUC Sport Scene database. The experiments demonstrate that the performance of the LDA model 1) is significantly lower than the SVM classifier for the scene classification over different types of features; and 2) decreases by fusing multiple features while improvement shown in SVM classifier.

In this study, the authors propose a novel methodology for static gesture recognition in a complex background using only depth map from Microsoft's Kinect camera. Four different types of features are extracted and analysed on two public static gesture datasets. The features extracted from the segmented hand are geometrical, local binary patterns, number of fingers ( Num ) raised in a gesture and distance of hand palm centre from the fingertips and the valley between the fingers. The hand region is first segmented from the image using depth data followed by the forearm removal. Four multi‐class support vector machine (SVM) kernels are also compared and used for recognition of gestures with extracted feature vector as an input. The experimental results achieved recognition accuracy of 99 and on two public complex static gesture datasets using Gaussian SVM kernel function as a classifier. The proposed approach is found to be comparable and even outperforms some of the state‐of‐the‐art techniques in terms of high recognition accuracies, even after using a single cue for hand segmentation and extraction of features in the complex background which results in non‐dependency on too many cues and much hardware.

Transductive transfer learning based Genetic Programming for balanced and unbalanced document classification using different types of features

Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming (GP) with a flexible representation can find the best solution without the use of domain knowledge. This article proposes a new GP-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators, and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualization of the learned features provide deep insights on the proposed approach.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in drug discovery. Multiple heterogeneous biological data provide abundant features for DTI prediction. Many computational methods have been proposed based on these data. However, most of these methods either extract features from sequences or from networks, utilizing only one aspect of the characteristics of drugs and targets, neglecting the complementary information between these two types of features. In fact, integrating different types of features will provide more valuable information for DTI prediction. In this article, we propose a novel method to improve the predictive capability for DTIs, named MFCADTI, by integrating multi-source feature through cross-attention mechanisms. The method extracts network topological features from the heterogeneous network and attribute features from sequences of drugs and targets. Considering the complementarity and heterogeneity between network and attribute features, cross-attention mechanisms are used to integrate the network and attribute features of drugs and targets. To capture the correlations between drugs and targets, cross-attention is used to learn the interaction features of each drug-target pair. We evaluate MFCADTI on two datasets and experimental results demonstrate a significant improvement in the performance of MFCADTI compared to state-of-the-art methods. Finally, case studies illustrate that MFCADTI is an effective DTI prediction way that provides valuable guidance for drug development. The data and source code used in this study are available at: https://github.com/Dejavun/MFCADTI.

Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses

Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI) classification tasks. Combining multiple features can describe the characteristics of pixels from different perspectives, and always results in better classification performance. Recently, multifeature combination learning has been widely employed to the multitask-learning-based representation-based model to obtain a multifeature representation vector. However, the linear sparse representation-based classifier (SRC) cannot handle the HSI with highly nonlinear distribution, and kernel sparse representation-based classifier (KSRC) can remedy the drawback of linear SRC. By adopting nonlinear mapping, the samples in kernel space are often of high or even infinite dimensionality. In this paper, we integrate kernel principal component analysis into multifeature-based KSRC and propose a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery. More specifically, spatial features, Gabor textures, local binary patterns, and difference morphological profiles are adopted and then each kind of feature is transformed nonlinearly into a new low-dimensional kernel space. The proposed framework can handle data with nonlinear distribution and add a dimensionality reduction stage in kernel space before optimizing the corresponding cost function. Experimental results on different HSIs demonstrate that the proposed MFKSRC algorithm outperforms the state-of-the-art classifiers.

Filtering of spam image-based email remains a major challenge for researchers. This paper presents a proposed work which is based on several facts such that spam images containing a large percentage of text which has characteristics or features different from other types of images. In addition to that, there is much similarity between the features of these images. These facts can be used to distinguish text regions spam images from others. A hybrid method based on combined features vector from text regions and features of the image is proposed. Two types of features are extracted. The first features extraction method is the local binary pattern (LBP) with extricating the image texture features directly, while the second is utilised to extricate features of image text regions only. The extracted features are used in individual and combination style in order to learn classifiers at the training stage. A one-class KNN classifier and two-class KNN classifier are applied separately. Each classifier was used in three fashion, with the text-regions features, with texture features in the image, and with merging both those features respectively. Experimental results showed that the appropriation of both image and text features together improves the effectiveness of the classification concerning the case in which only image or text features are used.

The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network

Feature-rank-code-based classifiers have been proposed recently in order to reduce the complexity for electronic nose system (ENS). The performance of these classifiers is degraded when the discriminatory information of gases lie in the actual feature values not in the ranks of features. To overcome the problem, a gas identification system based on a simple distance measure in combination with different type of features is proposed. In order to improve the computational cost of the existing ENS, a novel combination of optimum subset of features is proposed. To achieve the aim of low computational cost, a large feature vector is recursively reduced to a small number of features without compromising the classification accuracy of the system. Discrete binary particle swarm optimization, a metaheuristic for global search is used in a recursive setup to select the optimum subset of features for classification. Euclidian distance is used as a similarity measure for identification of different industrial gases. The proposed system is tested for the identification of 13 different industrial gases, namely, C3H8, Cl2, CO, CO2, SO2, NO2, NH3, C2H4O, C3H6O, C2H4, C2H6O, C7H8, and CH4. These gases are contained in three different data sets; two among these data sets are acquired experimentally in a laboratory setup, while one of these data sets is taken from the University of California at Irvine machine learning repository. Results reveal that the computational cost and the memory requirement of an ENS can be significantly reduced by combining different type of features. An average classification accuracy of 98.17% is achieved by the proposed system with an average 69.04% reduction in memory requirement using only three to five features.

A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings

On multi-type reverse nearest neighbor search