AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data

Nasimian, Ahmad; Younus, Saleena; Tatli, Özge; Hammarlund, Emma U.; Pienta, Kenneth J.; Rönnstrand, Lars; Kazi, Julhash U.

doi:10.1016/j.patter.2023.100897

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The majority of supervised algorithms exhibited comparable performances, with the exceptions of RANSAC and the Gaussian process which differed ( Figure 2B ). In terms of time efficiency, PCA emerged as the most proficient ( Figure 2C ); however, its performance lagged when evaluated based on the NegLog2RMSL (Nasimian et al, 2024 ) ( Figures 2D , E ). The comparative results suggested that supervised algorithms are relatively more efficient in the imputation process than most unsupervised algorithms, with NMF being the exception.…”

Section: Resultsmentioning

confidence: 97%

Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience

Younus,

Rönnstrand,

Kazi

2024

Front. Artif. Intell.

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The rapid proliferation of data across diverse fields has accentuated the importance of accurate imputation for missing values. This task is crucial for ensuring data integrity and deriving meaningful insights. In response to this challenge, we present Xputer, a novel imputation tool that adeptly integrates Non-negative Matrix Factorization (NMF) with the predictive strengths of XGBoost. One of Xputer's standout features is its versatility: it supports zero imputation, enables hyperparameter optimization through Optuna, and allows users to define the number of iterations. For enhanced user experience and accessibility, we have equipped Xputer with an intuitive Graphical User Interface (GUI) ensuring ease of handling, even for those less familiar with computational tools. In performance benchmarks, Xputer often outperforms IterativeImputer in terms of imputation accuracy. Furthermore, Xputer autonomously handles a diverse spectrum of data types, including categorical, continuous, and Boolean, eliminating the need for prior preprocessing. Given its blend of performance, flexibility, and user-friendly design, Xputer emerges as a state-of-the-art solution in the realm of data imputation.

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Section: Resultsmentioning

confidence: 97%

Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience

Younus,

Rönnstrand,

Kazi

2024

Front. Artif. Intell.

Self Cite

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MH-AutoML: Transparência, Interpretabilidade e Desempenho na Detecção de Malware Android

Assolin,

Canto,

Kreutz

et al. 2024

Anais Estendidos Do XXIV Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg Estendido 2024)

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A MH-AutoML é uma ferramenta de AutoML especializada na detecção de malware Android. Diferentemente de outras ferramentas de AutoML, a MH-AutoML incorpora recursos de transparência, interpretabilidade e depuração em todos os estágios do pipeline. A ferramenta também inclui métodos de seleção de caracteŕısticas espećıficos para o domínio e otimizações de hiperparâmetros que geram bons resultados. Os resultados indicam que a MH-AutoML produz modelos preditivos competitivos (e.g., 95% de recall com baixo custo computacional) em comparação com modelos gerados por outras 7 ferramentas de AutoML.

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AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data

Cited by 2 publications

References 34 publications

Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience

Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience

MH-AutoML: Transparência, Interpretabilidade e Desempenho na Detecção de Malware Android

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