Instance-based learning algorithms

By David William Aha, Dennis F. Kibler, Marc K. Albert. 2004

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Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, methodology): “Such learning algorithms include decision trees [37], SVMs [18], instance-based learning [1], etc.”
AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions (2021, methodology): “, can generate policy rules as well, K-nearest neighbors [142], Support vector machines [143], Adaptive boosting [144], Logistic regression [145], Stochastic Gradient Descent [146], or ensemble methods such as XGBoost [147], Random Forest learning [148], etc.”
Relational Instance Based Regression for Relational Reinforcement Learning (2003, background): “introduced the concept of instance based learning for classification (Aha et al., 1991) through the use of stored examples and nearest neighbor techniques.”
Text Classification by Combining Different Distance Functions withWeights (2006, methodology): “EIS, Vol. 127, No.12, 2007 よる分類精度と，従来の機械学習による知られている手法の分類精度を比較した結果を表 2 に示す。ここでは，5 節のコンバインアンサンブルのデータのインスタンスごとに 10 回の分類を行うという同じ条件で計算された Wilson らの機械学習の結果(1)と本研究での提案の異なる距離関数のコンバイン処理の数値結果をまとめて表 2 に示す。表 2 の C4.5 は帰納学習による 2 種類の決定木およびルールによる分類精度の結果を示しており，IB1 は k=1 の場合の最も簡単な kNN の手法の結果を示し，IB2 は訓練データを調整した結”
Retrieval, reuse, revision and retention in case-based reasoning (2005, unknown): “so-called heterogeneity problem that arises when different attributes are used to describe different cases (Aha, 1998; Aha et al., 2001; McSherry, 2001d; Stahl & Gabel, 2003; Bogaerts & Leake, 2004). Bogaerts & Leake (2004) propose and evaluate a variety of possible strategies for handling missing i”
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011, methodology): “We chose several well-known classifiers from different Machine Learning paradigms as base learners, namely, SVMs [73], decision trees [62], instance-based learning [1], fuzzy rule based systems [16] and decision lists [19].”
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection (2005, methodology): “We then show how similar benefits are obtained for the k-nearest neighbour algorithm [1] and Naı̈ve Bayes [11].”
Benchmarking Least Squares Support Vector Machine Classifiers (2004, methodology): “…(oneR) (Holte, 1993); statistical algorithms like linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (logit) (Bishop, 1995; Duda & Hart, 1973; Ripley, 1996); instance based learners (IB) (Aha & Kibler, 1991) and Naive Bayes (John & Langley, 1995).”
Concept drift detection via competence models (2014, methodology): “Therefore, this study focuses on concept drift detection for CBR.”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, methodology): “The k NN classiﬁer employed in this work follows the implementation described in [47].”
Optimal instance selection for improved decision tree (2007, background): “This is due to the large number of possible branches (2) and more importantly, the inability to find a good relaxed problem because of the complexity of the decision trees.”
A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform (2017, background): “5 [43], functional tree (FT) [44], Bayes net [45], Naive-Bayes [46], and K-nearest neighbors (K-NN) [47] classifiers that are available at Waikato environment for knowledge analysis software [48].”
A novel ensemble method for classifying imbalanced data (2015, methodology): “5 [33], RIPPER [34], Random Forest [35], SMO [36], and IBK [37] were evaluated over 46 highly imbalanced data sets.”
A study of the behavior of several methods for balancing machine learning training data (2004, methodology): “This distance function uses the Euclidean distance for quantitative attributes and the VDM distance [21] for qualitative attributes.”
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels (2017, background): “Traditional learning performance degrades monotonically with label noise (Aha et al., 1991; Nettleton et al., 2010), necessitating semi-supervised approaches (Blanchard et al., 2010).”
Re-examining Machine Translation Metrics for Paraphrase Identification (2012, methodology): “We used three constituent classiﬁers: Logistic regression, the SMO implementation of a support vector machine (Platt, 1999; Keerthi et al., 2001) and a lazy, instance-based classiﬁer that extends the nearest neighbor algorithm (Aha et al., 1991).”