two-class classification problem
Efficient Altered Fingerprint Analysis and Rectification of Distorted Fingerprint
... label classification can give best feature. In positive classification, where the physical access control systems and user should negotiate for self- ...case classification broadly talks about low ...
5
A Corpus based Approach to Automatic Compound Extraction
... It uses n-gram mutual information, relative frequency count and parts of speech as the features for compound extraction.. The problem is modeled as a two-class classification problem bas[r] ...
6
Evaluation of Classification Algorithms with Solutions to Class Imbalance Problem on Bank Marketing Dataset using WEKA
... research problem in data mining classification algorithm , that is class imbalance problem, which happens when one of the two classes has very less number of samples compared to the ...
8
Ordinal Classification Method for the Evaluation Of Thai Non-life Insurance Companies
... multi-class classification model consists of the priority for further examination (High, Medium, and Low ...into two cases; two-class classification (healthy and insolvency) and ...
5
An Approach for Class Imbalance Using Oversampling Technique
... gives Class imbalance problem which become greatest issue in data ...Imbalance problem occur where one of the two classes having more sample than other ...on classification of major ...
5
Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem
... convolutional, two pooling and two fully connected ...imagery problem have been proposed over the years in ...a two-class motor imagery ...Average classification rate of 78% was ...
14
Using Output Codes for Two-class Classification Problems
... for two-class classification ...to two-class situations. The problem is that we can correct only d−1 2 (rounded down) errors with the ECOC prediction scheme when the row ...
118
A Review of Class Imbalance Problem
... parallel classification method to improve classifying speed; two classifiers (simple one and complicated one) were trained serially but worked in ...
9
Classification of two parameter bifurcations
... this problem can be reduced to studying the action of an algebraic group on a finite dimensional vector ...equivalence class depends only on a finite number of its Taylor ...
164
Data mining of range-based classification rules for data characterization
... All methods applied on continuous data that do not require pre-discretization, perform a type of split of the data, thus generating numerical ranges. In existing solutions, this is a binary split, in a form of a relation ...
136
Absent Data Generating Classifier for Imbalanced Class Sizes
... a problem for ADG than that for BSMOTE, which also aims at generating data close to the ...imbalanced classification in higher dimensions as ...imbalanced classification, and we think its strength ...
30
Enhancing Confusion Entropy (CEN) for Binary and Multiclass Classification
... It is remarkable that CEN shows to have a weakness in the binary case that invalidates it as a suitable performance measure: in some situations CEN gets values larger than one, unlike what happens in the ...
30
Identity authentication using improved online signature verification method
... A comprehensive survey of signature verifica- tion can be found in (Leclerc and Plamondon, 1994; Plamondon and Lorette, 1989). Signature verification systems differ both in their feature selection and their decision ...
9
Recognition of Sentiment Sequences in Online Discussions
... ics contained < 10 posts. These short topics often contained one initial request and a couple of replies and were deemed too short to form a good discussion. We also excluded topics containing > 20 posts. This ...
6
COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images
... Abstract—Coronavirus disease (COVID-19) is a pandemic infectious disease that has a severe risk of spreading rapidly. The quick identification and isolation of the affected persons is the very first step to fight against ...
5
ADVANCED DETECTOR FOR INVASIVE DUCTAL CARCINOMA USING DEEP LEARNING
... The utilization of deep learning intelligence system to redefine medical assistance and support for decision making is shifting the paradigm of disease diagnosis. Clinical grade systems that are able to automate the ...
11
LSTM neural network implementation using memristive crossbar circuits and its various topologies
... classification problem. This is because we are not interested in the analog output voltage – as long as it is high enough or low enough, we know that it is either digital 1 or digital 0. Whereas, in the ...
75
Semi-Supervised Novelty Detection
... NP classification translates to a finite sample bound for SSND and leads to desirable properties such as ...compares two objective functions and is agnostic to the method ...
37
MINING ACCESS PATTERNS USING CLASSIFICATION
... In day to day life, we see many advertisements aimed at attracting more customers and also changes in marketing schemes. These are done by a company after immense research in the market about the customer. Since, ...
8
Analyzing and Pruning Ensembles Utilizing Bias and Variance Theory.
... Although there has been a lot of research conducted on the encoding and decoding (testing) strategies of ECOC ensembles, they are still open problems. In this thesis we firstly investigate two new methods, ...
160