Search results for "Classifier"
showing 10 items of 231 documents
Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones
2014
Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In …
QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study
2010
Abstract Cluster analysis (CA), Linear and Quadratic Discriminant Analysis (L(Q)DA), Binary Logistic Regression (BLR) and Classification Tree (CT) are applied on two datasets for description of tyrosinase inhibitory activity from molecular structures. The first set included 701 tyrosinase inhibitors (TI) that are used for performance of inhibitory and non-inhibitory activity and the second one is for potency estimation of active compounds. 2D TOMOCOMD-CARDD atom-based quadratic indices are computed as molecular descriptors. CA is used to “rational” design of training (TS) and prediction set (PS) but it shows of not being adequate as classification technique. On the first data, the overall a…
Classification of the hadronic decays of the Z$^0$ into b and c quark pairs using a neural network
1992
A classifier based on a feed-forward neural network has been used for separating a sample of about 123 500 selected hadronic decays of the Z 0 , collected by DELPHI during 1991, into three classes according to the flavour of the original quark pair: u u +d d +s s (unresolved), c c and b b . The classification has been used to compute the partial widths of the Z 0 into b and c quark pairs. This gave Γ c c /Γ h = 0.151 ± 0.008 ( stat. ) ± 0.041 ( syst. ) , Γ b b /Γ h = 0.232±0.005 ( stat. )±0.017 ( syst. ) .
Detection and Recognition of Target Signals in Radar Clutter via Adaptive CFAR Tests
2006
In this paper, adaptive CFAR tests are described which allow one to classify radar clutter into one of several major categories, including bird, weather, and target classes. These tests do not require the arbitrary selection of priors as in the Bayesian classifier. The decision rule of the recognition techniques is in the form of associating the p-dimensional vector of observations on the object with one of the m specific classes. When there is the possibility that the object does not belong to any of the m classes, then this object is to be classified as belonging to one of the m classes or to class m+1 whose distribution is unspecified. The tests are invariant to intensity changes in the …
Dynamic integration of classifiers in the space of principal components
2003
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the integration procedure in the ensemble should properly utilize the ensemble diversity. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be pr…
Feature selection using ROC curves on classification problems
2010
Feature Selection (FS) is one of the key stages in classification problems. This paper proposes the use of the area under Receiver Operator Characteristic curves to measure the individual importance of every input as well as a method to discover the variables that yield a statistically significant improvement in the discrimination power of the classification model.
A genetic algorithm approach to purify the classifier training labels for the analysis of remote sensing imagery
2017
This paper proposes a Genetic Algorithm (GA) approach to clean a given classifier training set for remote sensing image analysis. Starting from an initial set of training data, the new method called GA-Training Label Purifying (GA-TLP) consists of the significant training sample selection using GAs in order to maximize the classifier accuracy. This means to retain the most informative samples and to remove the uncertain, redundant, and misclassified ones. As a result of the selection process, we can obtain a purified training set. The proposed model is implemented and evaluated using a LANDSAT 7 ETM+ image. The experimental results confirm the effectiveness of the proposed approach.
Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains
2021
This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…
A neural multi-agent based system for smart html pages retrieval
2003
A neural based multi-agent system for smart HTML page retrieval is presented. The system is based on the EalphaNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve documents satisfying a query and dealing with a specific topic. The system has been developed using the basic features supplied by the Jade platform for agent creation, coordination and control. The system is composed of four agents: the trainer agent, the neural classifier mobile agent, the interface agent, and the librarian agent. The sub-symbolic knowledge of the neural classifier mobile agent is automatically …
Using active learning to adapt remote sensing image classifiers
2011
The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and cluster…