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Multi-class confidence weighted algorithms
2009
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09
unpublished
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The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. ...
We derive learning algorithms for the multi-class CW setting and provide extensive evaluation using nine NLP datasets, including three derived from the recently released New York Times corpus. ...
In this work, we develop and test multi-class confidence weighted online learning algorithms. ...
doi:10.3115/1699571.1699577
fatcat:nmtw7wqxczhbzewu6i7z632iva
Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network
2012
International Journal of Computer Applications
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This paper is based on MrCAR (Multi-relational Classification Algorithm) and Kohonen's Self-Organizing Maps (SOM) approach. ...
SOM is a class of typical artificial neural networks (ANN) with supervised learning which has been widely used in classification tasks. ...
associative classification algorithm) A multi-relational associative classification algorithm has the idea of class recurrent closed itemset [17] . ...
doi:10.5120/4979-7237
fatcat:w2wbo6lpwbehnpzxsqny44c5yi
Data for Fish Stock Assessment Obtained from the CMSY Algorithm for all Global FAO Datasets
2019
Data
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Recently, an algorithm (CMSY) has been proposed, allowing an estimation of stock assessment variables from catch and resilience. ...
These data come from the CMSY algorithm for 42% of the stock (75% of the global reported fish catch) and are estimated by aggregated values for the remaining 58%. ...
For class IV, the weighted median of corresponding stocks is used.
Traceability of Algorithm Runs The two input files of the CMSY algorithm are provided. ...
doi:10.3390/data4020078
fatcat:47oxerkkordklkgmcdslcxxwvm
Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments
[chapter]
2010
Lecture Notes in Computer Science
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A parametric generalization of the popular RELIEF machine learning algorithm for weighting residues is introduced and incorporated in multi-RELIEF. ...
The ensemble criterion of multi-RELIEF for merging the weights of multiple runs is simplified. ...
Setting the stage: the Multi-RELIEF approach Multi-RELIEF uses as core procedure RELIEF [11] , a successful two-class feature weighting algorithm, and an ensemble approach for handling multiple classes ...
doi:10.1007/978-3-642-12211-8_14
fatcat:q6biditucnb2zbztjn36yordoa
Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach
2013
International Journal of Applied Information Systems
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A multi-objective genetic algorithm approach is proposed in this paper for the discovery of interesting association rules with multiple criteria i.e. support, confidence and simplicity (comprehensibility ...
the user-specified thresholds of minimum support and minimum confidence. ...
=1 →
class=Right
0.040 0.960
0.500
0.423
4
left-weight=1
right-weight=5 →
class=Right
0.040 0.960
0.500 0.423
5
left-weight=5
left-distance=5 →
class=Left
0.040 0.960
0.500 0.423 ...
doi:10.5120/ijais12-450873
fatcat:ci5gzpei7vanbnqjxpb6gmxmtq
Association on Supervised Term Weighting Method for Classification on Data Twitter
2020
International journal of recent technology and engineering
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in a class by using confidence values. ...
optimal for the multi-class classification. ...
[11] proposes an adaptive weighting of keyword positions following the traditional TF-IDF algorithm, called the TF-IDF-AL Algorithm. ...
doi:10.35940/ijrte.f6975.038620
fatcat:duexbm36v5cgdnpjh4t2udw6iy
A PSO algorithm for improving multi-view classification
2011
2011 IEEE Congress of Evolutionary Computation (CEC)
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It also considers that some views may be better at classifying specific classes, and provides weighting schemes for both views and classes. ...
This paper proposes a PSO algorithm to combine the outputs coming from different views. ...
The role of the weights W i and W ij is to modify the confidence φ v of the classifiers ϕ v in predicting the class of an instance e according to this profile. ...
doi:10.1109/cec.2011.5949717
dblp:conf/cec/JuniorP11
fatcat:dpdn5hzrt5bitoubsvpzger32m
ConfDTree: Improving Decision Trees Using Confidence Intervals
2012
2012 IEEE 12th International Conference on Data Mining
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This method, which can be applied on any decision trees algorithm, uses confidence intervals in order to identify these hard-to-classify instances and proposes alternative routes. ...
The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class ...
Weighted average AUC is presented for multi-class datasets in parentheses. RandomForest is also presented, as an upper-bound. ...
doi:10.1109/icdm.2012.19
dblp:conf/icdm/KatzSRO12
fatcat:z6apzb4lb5axtidqaptzu4epza
An Online Framework for Learning Novel Concepts over Multiple Cues
[chapter]
2010
Lecture Notes in Computer Science
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In the first layer, we use a budget online learning algorithm for each single cue. Thus, each classifier provides confidence interpretations for target categories. ...
We test our algorithm on two student-teacher experimental scenarios and in both cases results show that the algorithm learns the new concepts in real time and generalizes well. ...
For multi-class problems, the algorithm is extended using the multi-class extension method presented in [17] ; we omit the detailed derivation for lack of space. ...
doi:10.1007/978-3-642-12307-8_25
fatcat:gnyhulhk5ndw3crjyiabht5ib4
Self-paced Multi-view Co-training
2020
Journal of machine learning research
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Furthermore, the SPamCo algorithm is proved to be PAC learnable, supporting its theoretical soundness. ...
Besides, most of the traditional co-training approaches are implemented for two-view cases, and their extensions in multi-view scenarios are not intuitive. ...
In this way, classifiers of all views are trained in parallel when multi-views or multi-models are available. • The effectiveness of the proposed SPamCo algorithms under multi-view cases is analyzed based ...
dblp:journals/jmlr/MaMD020
fatcat:btvd2npcgffc5cv26jzeyorhna
Multiple labels associative classification
2005
Knowledge and Information Systems
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In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multilabel associative classification approach (MMAC). ...
In addition, four measures are presented in this paper for evaluating the accuracy of classification approaches to a wide range of traditional and multi-label classification problems. ...
This paper introduces a novel approach for multi-label classification, named multi-class multi-label associative classification (MMAC). ...
doi:10.1007/s10115-005-0213-x
fatcat:o5v6vnaegncovbefj6kddfaw4m
Online Spectral Learning on a Graph with Bandit Feedback
2014
2014 IEEE International Conference on Data Mining
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Second, we present an online multi-class classification algorithm with bandit feedback. We use upper-confidence bound technique to trade off the exploration and exploitation of label information. ...
Experiments on several benchmark graph datasets show that the proposed online multi-class classification algorithm beats the state-of-art baseline, and the proposed bandit algorithm is also much better ...
The algorithm maintains c weight vectors w k ∈ R d , k = 1, . . . , c. ...
doi:10.1109/icdm.2014.72
dblp:conf/icdm/GuH14
fatcat:ni7mhvykozh2bn7rridxgmombm
Semi-supervised multi-label image classification based on nearest neighbor editing
2013
Neurocomputing
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confident samples during the course of semi-supervised learning. ...
In order to reduce this negative effect, the nearest neighbor data editing technique is introduced to semi-supervised multi-label classification, and thus an algorithm named Multi-Label Self-Training with ...
labels based on the distribution of class labels in a multi-label dataset. ...
doi:10.1016/j.neucom.2013.03.011
fatcat:w45ppwtllzdphdohasniitpzyq
PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining
2016
International Journal of Computer Applications
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This paper presents classification rule mining as a multi-objective problem rather than a single objective one. Multi-Objective optimization is a challenging area and focus for research. ...
In this paper PSO is taken as taken as a swarm intelligence algorithm and classification rule mining is taken as the problem domain. ...
The pseudocode for general MOPSO is illustrated in algorithm. Algorithm: General Multi-objective Particle Swarm Optimization Algorithm 01. Begin 02. Parameter Settings and initialize Swarm 03. ...
doi:10.5120/ijca2016908697
fatcat:4jvvnqkxufhxxl34ijh3kd6f44
Multi-label rules for phishing classification
2015
Applied Computing and Informatics
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Current AC algorithms produce only the largest frequency class connected with a rule in the training data set and discard all other classes even though these classes have data representation with the rule's ...
This algorithm discovers rules associated with a set of classes from single label data that other current AC algorithms are unable to induce. ...
(Items, Classes) contained within the rule, whereas, MMAC assigns the top ranked class confidence and support to the multi-label rule. ...
doi:10.1016/j.aci.2014.07.002
fatcat:lriyxszokzc7bjqgh3ciqk7e2u
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