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In this article, a clustering-based band selection method is proposed to tackle the dimension reduction problem of hyperspectral data. The method is essentially based on low-rank doubly stochastic matrix decomposition, which is more stable than current low-rank approximation clustering methods. Experimental results show that the selected band subsets perform well in hyperspectral data classification...
We propose a new learning algorithm of latent local support vector machines (SVM), called Latent-lSVM for effectively classifying very-high-dimensional, large-scale multiclass image datasets. The common framework of image classification tasks using the Scale-Invariant Feature Transform method (SIFT), the Bag-of-visual-Words (BoW), leads to hard classification problem with thousands of dimensions,...
This paper proposes an approach using MapReduce-based Rocchio relevance feedback algorithm, which improved the traditional Rocchio algorithm in the MapReduce paradigm, to resolve the problem of massive information filtering. Traditional text classification algorithms have vital impact on information filtering.
Nowadays, the number of internet users in Indonesia is increasing rapidly. This condition leads to possibility to use and analyze the data gathered from the internet users to show the big picture of the specific condition in certain region. Currently, there are some research on analyzing public mood, such as happy, sad, anger, fear, and neutral. The analysis itself consists of mood classification...
Figure-Ground Segmentation simply means separating foreground from it's background. It has many applications in day to day life. Object recognition is one of the main applications of it. Extracting foreground from it's background is not an easy task. Various techniques are available for figure-ground segmentation. In this paper, a new approach is proposed to extract foreground from background. A high...
Network traffic in the world wide is calculated to rise every year twice the times. To keep pace and profit from this increased amount of flows efficiently. And offer new services. Some efficient techniques needed. Day by day new applications are invented and they have heterogeneous nature in network environment and communication between these new devices also a critical part. improving the network...
Intrusion detection systems monitor network or host packets in an attempt to detect malicious activities on a system. Anomaly detection systems have success in exposing new attacks, commonly referred to as ‘zero’ day attacks, yet have high false positive rates. False positive events occur when an activity is flagged for investigation yet it was determined to be benign upon analysis. Computational...
This paper proposes an under-sampling method with an algorithm which guarantees the sampling quality called k-centers algorithm. Then, the efficiency of the sampling using under-sampling method with k-means algorithm is compared with the proposed method. For the comparison purpose, four datasets obtained from UCI database were selected and the RIPPER classifier was used. From the experimental results,...
In this paper, we proposed a novel medical images based computer aided diagnosis method named ECARMI. It combines the cost-sensitive learning with selective ensemble techniques to improve the medical images based diagnosis performance. At first, selective cost-sensitive SVM ensemble is utilized to perform the classification of medical images. Then, the Regions of Interest (ROIs) in positively identified...
We present an approach for aspect based opinion mining, which uses an unsupervised neural network as the opinion classifier. To identify the aspects, we use the Ant Clustering Algorithm. It is able to group similar sentences into clusters and then to extract from each cluster one different aspect of the opinion target object. The neural model used for sentiment analysis is an extension of the Growing...
This paper presents the improved algorithm for the Hybrid Approach of Neural network and Level-2 Fuzzy set (HANN-L2F). The main structure is including 2 parts. The first part is Neuro-Fuzzy system, including the MLP Neural network with the combination of the level-2 Fuzzy system. The second part is using k-nearest neighbor to classify the output from Neuro-fuzzy. The HANN-L2F is an algorithm with...
Traditionally, only experts who are equipped with professional knowledge and rich experience are able to recognize different species of wood. Applying image processing techniques for wood species recognition can not only reduce the expense to train qualified identifiers, but also increase the recognition accuracy. In this paper, a wood species recognition technique base on Scale Invariant Feature...
In the present work, a neoteric image segmentation technique has been framed, which is stood on color of the image using an unsupervised K-means clustering. The color image is converted into Lab (L=luminocity layer; a=chromaticity layer 1; b = chromaticity layer2) in various computational steps and each layer has its own importance. Clustering is a process to distinguish different kind of objects...
Training fault detection model requires advanced data-mining algorithms when the growth rate of the process data is notably high and normal-class data overwhelm fault-class data in number. Most standard classification algorithms, such as support vector machines (SVMs), can handle moderate sizes of training data and assume balanced class distributions. When the class sizes are highly imbalanced, the...
The new hybrid sampling approach called CLUS- CLUSter-based hybrid sampling approach is proposed in this paper to improve the performance of classifier for two-class imbalanced datasets. The objective of this research is to develop algorithm that can effectively classify two-class imbalanced datasets, which have complicated distributions and large overlap between classes. These problems can make the...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
This paper focuses on detecting and classifying pole-like objects from point clouds obtained in urban areas. To achieve our goal, we propose a system consisting of three stages: localization, segmentation and classification. The localization algorithm based on slicing, clustering, pole seed generation and bucket augmentation takes advantage of the unique characteristics of pole-like objects and avoids...
The objective of the present work is to design a HADOOP based parallel Marathi content retrieval system using clustering technique to get the efficient and optimized result than existing systems. The system also focuses on providing the personalized documents in Marathi language to the end user based on their interests identified from the browsing history and using time session mechanism for re ranking...
Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. An acquaintance of a customer is more costly than retainment of an existing customer. Churn predictions shed light on members about to leave the service and support promotion...
This paper proposes a chaotic particle swarm optimization and kernel matching pursuit algorithm a combination of speaker recognition methods. First through the CPSO clustering algorithm to transform the MFCC feature parameters of processing, streamlining of the MFCC feature parameters (SMFCC), then use the KMP algorithm on the reduction parameters after the SMFCC feature classification training and...
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