Classification Algorithm

Data preprocessing plays an important role in many processes of data mining. The practice widely adopted in this area is only to use a preprocessing method like discretization. In this paper we propose an ordered scheme to combine various important methods of data preprocessing. The aim is to increase the accuracy of the most used classification algorithms. The experimental results

— In high-energy physics, with the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data. Multivariate classification methods based on machine learning techniques have ...

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose of adapting the system to an on-line grading machine. In particular, this work was focused on studying the feasibility of identifying organic stains on brown eggshells (dirty eggshell) caused by blood, feathers, feces, etc., from natural stains caused by deposits of pigments on the outer layer of clean eggshells. During the analysis, a total of 384 eggs were evaluated (clean, 148; dirty, 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the color of the natural stains (clear or dark). For each sample, digital images were acquired by employing a charged coupled device camera endowed with 15 monochromatic filters (440–940 nm). A Matlab® function was developed in order to automate the process and analyze images with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the image samples into the dirty or clean group on the basis of the geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify nearly 98% of the samples with a very low processing time (0.05 s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining a similar percentage of correctly classified samples (97%).

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.

Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. There is great diversity in document image classifiers: they differ in the problems they solve, in the use of training data to construct class models, and in the choice of document features and classification algorithms. We survey this diverse literature using three components: the problem statement, the classifier architecture, and performance evaluation. This brings to light important issues in designing a document classifier, including the definition of document classes, the choice of document features and feature representation, and the choice of classification algorithm and learning mechanism. We emphasize techniques that classify single-page typeset document images without using OCR results. Developing a general, adaptable, high-performance classifier is challenging due to the great variety of documents, the diverse criteria used to define document classes, and the ambiguity that arises due to ill-defined or fuzzy document classes.

Human's safety in construction areas is vital. A hard hat is required to enter a construction area. Stopping a person who is not wearing a hard hat entering a construction area is very important. Video-based surveillance to detect hard hat is a new solution to this ...

To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antidepressants conducted by the Food and Drug Administration (FDA). Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of "suicide attempt," a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA's audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs.

In this paper, a methodology for the automated detection and classification of Tuberculosis(TB) is presented. Tuberculosis is a disease caused by mycobacterium which spreads through the air and attacks low immune bodies easily. Our methodology is based on clustering and classification that classifies TB into two categories, Pulmonary Tuberculosis(PTB) and retroviral PTB(RPTB) that is those with Human Immunodeficiency Virus (HIV) infection. Initially K-means clustering is used to group the TB data into two clusters and assigns classes to clusters. Subsequently multiple different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. This methodology is evaluated using 700 raw TB data obtained from a city hospital. The best obtained accuracy was 98.7% from support vector machine (SVM) compared to other classifiers. The proposed approach helps doctors in their diagnosis decisions and also in their treatment p...

Abstract—The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (eg, late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is ...

Automatic modulation classification is an intrinsically interesting problem with various civil and military applications. A generalized digital modulation classification algorithm has been developed and presented in this paper. The proposed algorithm uses Genetic Programming (GP) with K-Nearest Neighbor (K-NN). The algorithm is used to identify BPSK, QPSK, 16QAM and 64QAM modulations. Higher order cumulants have been used as input features