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Multi-class confidence weighted algorithms

Koby Crammer, Mark Dredze, Alex Kulesza
2009 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09   unpublished
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

Ram PrakashGangwar, Jitendra Agrawal, Varsha Sharma
2012 International Journal of Computer Applications  
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  
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]

Elena Marchiori
2010 Lecture Notes in Computer Science  
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

Basheer MohamadAl-Maqaleh
2013 International Journal of Applied Information Systems  
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  
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

Zilton Cordeiro, Gisele L. Pappa
2011 2011 IEEE Congress of Evolutionary Computation (CEC)  
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

Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek
2012 2012 IEEE 12th International Conference on Data Mining  
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]

Luo Jie, Francesco Orabona, Barbara Caputo
2010 Lecture Notes in Computer Science  
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

Fan Ma, Deyu Meng, Xuanyi Dong, Yi Yang
2020 Journal of machine learning research  
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

Fadi Abdeljaber Thabtah, Peter Cowling, Yonghong Peng
2005 Knowledge and Information Systems  
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

Quanquan Gu, Jiawei Han
2014 2014 IEEE International Conference on Data Mining  
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

Zhihua Wei, Hanli Wang, Rui Zhao
2013 Neurocomputing  
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

Anil Kumar
2016 International Journal of Computer Applications  
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

Neda Abdelhamid
2015 Applied Computing and Informatics  
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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