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In Machine Learning applications, the selection of the classification algorithm depends on the problem at hand. This paper provides a comparison of the performance of the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) for food intake detection. A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were...
Along with the increase number of users for the credit, the screening of applicants becomes very significant. If the credit of applicants is bad, the bank will obtain a great loss. Support vector machine (SVM) is one of the most popular kinds of algorithms for the new consumer's credit approval. However, there is a disadvantage that the more close to the optimal hyper plane, the greater possibility...
Identification of unsolicited emails (spams) is now a well-recognized research area within text classification. A good email classifier is not only evaluated by performance accuracy but also by the false positive rate. This research presents an Enhanced Genetic Programming (EGP) approach which works by building an ensemble of classifiers for detecting spams. The proposed classifier is tested on the...
Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of...
The orientation of sentiment words plays an important role in the sentiment analysis, but existing methods have difficulty in classifying the orientation of Chinese words, especially for the newly emerged words in Internet. Most approaches are mining the association between sentiment words and seed words using the big corpora and manually labeled seed words with definite orientation. But less work...
The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.
When SVM is adopted for image annotation, we know that high quality sample images will improve image recognition accuracy. Images with the same visual/semantic features are adopted as positive sample images, and images with different visual/semantic features are adopted as negative sample images. But it is labor intensive in high quality sample images selection, especially when collecting by visual...
Detecting emotional traits in call centre interactions can be beneficial to the quality management of the services provided, since this reveals the positioning of both speakers, i.e. satisfaction or frustration and anger on the customers' side, and stress detection, disappointment mitigation or failure to provide the requested service on the operators' side. This paper describes a machine learning...
In this paper, we present a novel fall detection method using wearable sensors that are inexpensive and easy to deploy. A new, simple, yet effective feature extraction scheme is proposed, in which features are extracted from slices or quanta of sliding windows on the sensor's continuously acceleration data stream. Extracted features are used with a support vector machine model, which is trained to...
This paper presents an improved method of selective ensemble to filter the spam messages. The design adopts clustering based on the diversity between sub-classifiers to solve the problem of selection. To improve accuracy and stability, a conception of confidence weight is proposed to evaluate the reliability of selected sub-classifiers. The training model is created with small datasets as in the real...
This paper proposes a new signature verification technique called combined segmentation-verification based on off-line features and on-line features. We use three different off-line feature vectors extracted from full name Japanese signature image and from the sub-images of the first name and the last name. The Mahalanobis distance for each offline feature vector is calculated for signature verification...
A steganography detection algorithm on JPEG images-Markov detection is introduced. On the basics of Markov steganography detection algorithm, we detect many kinds of images, such as stego-images, spliced images and median filtered images and so on. The experiment results show that the improved Markov algorithm has higher detection accuracy rate for many kinds of images.
Intelligent Transportation System is a worldwide research hotspot and the extraction of traffic parameters is a crucial part of it for subsequent identification of traffic states. This paper proposes a novel approach of extracting traffic parameters such as time occupancy, volume and vehicle velocity based on video images. Visual features obtained from spatio-temporal images are more immune to environmental...
With the development of digital image processing technology, image capture and image tampering are easy to obtain with the help of portable devices and software tools. Subsequently, digital image forensics has become increasingly important, in which recaptured image detection is one branch. In this paper, a set of features based on image texture are used to identify the recaptured images. Because...
Chronic lymphocytic leukemia (CLL) is the most common type of blood cancer in Canadian adults. The relative 5-year survival rates for CLL in Canada is decreasing. CLL cell morphology maybe similar to normal lymphocytes and require a hematopathologist examination for diagnosis. There are a low number of related works on image analysis in CLL. This paper focuses on lymphocyte color cell segmentation...
This paper presents a machine learning-based faulty-line identification method in smart distribution networks. The proposed method utilizes postfault root-mean-square (rms) values of voltages measured at the main substation and at selected nodes as well as fault information obtained by fault current identifiers (FCIs) and intelligent electronic re-closers (IE-CRs). The information from FCIs and IE-RCs...
In this paper, we present a weight learning method introduced to learn weights on each individual classifier to construct an ensemble. Genetic algorithm is applied to search for an optimal combination of weights for each individual classifier on which classifier ensemble is expected to give best performance. Our proposed ensemble approach can combine heterogeneous classifiers and/or classifier ensembles...
In this paper, we introduced a classifier ensemble approach to combine heterogeneous classifiers in the presence of class label noise in the datasets. To enhance the performance of classifier ensemble, we give a preprocessing approach to filter out this class label noise. The filtered data is then used to learn individual classifier model. After that, a weight learning method is introduced to learn...
Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors are very promising. These devices open wide range of opportunities of using their...
Polycystic Ovary Syndrome (PCOS) is a female endocrine disorder which severely affects women's health and its diagnostic requires medical treatment or even surgery. Manual analysis of PCOS diagnosis often produces errors. Recently, many automated algorithms have been studied for polycysts detection in Ultrasound images. This paper presents cysts detection and classification in the ovary ultrasound...
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