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Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the...
This paper compares the performance improvement in recognition rate of different face recognition methods. The face recognition methods such as 1dPCA, 2dPCA, KPCA, ICA and FDA usually use Euclidean distance and in some cases they use the cosine similarity function. Instead of traditional classification and distance measurement methods, SVM classifier is used for classification. The SVM classifier...
In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the binary classification task where each paragraph or sentence is classified into the essence or non-essence, and in previous works,...
In this research, we propose the version of K Nearest Neighbor which considers similarity among attributes for computing the similarity between feature vectors. The text segmentation task is viewed into the binary classification where each pair of sentences or paragraphs is classified into whether we put the boundary or not, and the proposed version resulted in the successful results in previous works...
Binary classification problem is one of the mainstream research in pattern recognition field. This study proposed a modified fruit-fly optimization algorithm (FOA), which can find an eligible begin location of the FOA as starting location before running the FOA's procedure, and in the FOA's processing, the SVM parameters is modified by dynamically updating the location of each fruit-fly and the optimal...
Opinions about the data has been an important part of analyzing the opinions and sentiments. The sentiment analysis is a major part of data mining that has important applications in various fields. The novice customers get into any field by only getting reviews from the various websites or reviewers. The reviews are not necessarily correct all the time. So, we need to first analyze them and then put...
Many disorders can be diagnosed by analysis of gene expression microarrays and this can save lots of lives. However, as gene expression data have high dimensions, establishing a method to identify the genes related to the target disease still remains a challenge, because it should provide a well-grounded prediction about the disease status. To this end, the best subset of genes should be distinguished...
A challenge is indexing the facial beauty by a machine as same evaluated by human beings. A question arises: Can beauty be learnt by machines? Every individual have different concept of facial beauty. Somebody can be attracted by someone but might not be by another person. In recent past, many psychologists, neurologists and other scientists have done tremendous work in this area. This work presents...
Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for...
Occupational accident is a serious issue for every industry. Steel industry is considered to be one of the economic sectors having a high number of accidents. Thus, the main aim of this study is to build a model which could predict the occupational incidents (i.e., injury, near-miss, and property damage) using support vector machine (SVM) by utilizing a database comprising almost 5000 occupational...
One of the main tasks sought after with machine learning is classification. Support vector machines are one of the widely used machine learning algorithms for data classification. SVMs are by default binary classifiers, extending them to multi-class classifiers is a challenging on-going research problem. In this paper, we propose a new approach to constructing the multi-class classification function,...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
This paper studies on the Day-of-the-week effect by means of several binary classification algorithms in order to achieve the most effective and efficient decision trading support system. This approach utilizes the intelligent data-driven model to predict the influence of calendar anomalies and develop profitable investment strategy. Advanced technology, such as time-series feature extraction, machine...
The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved...
For distinguishing outliers from targets and locating dot matrix character, outlier detection with mean shift trail outlier factor (MSTOF) is proposed to indicate the score of outlier-ness. Firstly, k-distance neighborhood of an object is employed and k-mean shift trail vector of an object is established in terms of the difference between the near two k-mean shift vectors. Secondly, k-average mean...
A new edge-directional image resampling method is proposed. The method uses a weighted sum of two adaptive 4x4 interpolation kernels to construct high-resolution pixels. The weights are chosen according to the local gradient features for each pixel. The interpolation kernels are learned using pairs of low- and high-resolution images taken from LIVE image database. The method has low complexity and...
Feature selection and parameters optimization is an important step in the using of SVM. In recent years, more researchers are mainly focus in feature selection and parameters optimization. However, the number of support vectors with the selected support vector subset also has an effect on classification performance of SVM. Few researchers concentrate on this area. This paper proposed a novel optimization...
With the advent of large numbers of data and a large number of samples, the traditional support vector machine algorithm is not applicable because of it's too much memory overhead and time overhead. Traditional parallel SVM based on MapReduce is to separate the train data into multiple sub-training sets on MapReduce-based model, these sub-datasets are trained by SVM, and then, get the support vectors...
Support Vector Machine (SVM) is one of widely-used text classification method. Although SVM performs well in practice, SVM encounters two problems: the data distribution is not taken into consideration in the process of classification and its performance is greatly influenced by noises. In view of this, Fuzzy Support Vector Machine based on Manifold Discriminant Analysis (FSVM-MDA) is proposed and...
Water is classified into four status of water quality, which good condition, lightly polluted, medium polluted and heavyly polluted. The classification status of water quality is very important to know the proper use and handling. Accuracy in classification of the quality status is very important, so that both of the classification algorithm K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)...
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